Scoliosis often occurs in adolescents and seriously affects physical development and health. Traditionally, medical imaging is the most common means of evaluating the corrective effect of bracing during treatment. However, the imaging approach falls short in providing real-time feedback, and the optimal corrective force remains unclear, potentially slowing the patient's recovery progress. To tackle these challenges, an all-in-one integrated array of pressure sensors and sEMG electrodes based on hierarchical MXene/chitosan/polydimethylsiloxane (PDMS)/polyurethane sponge and MXene/polyimide (PI) is developed. Benefiting from the microstructured electrodes and the modulus enhancement of PDMS, the sensor demonstrates a high sensitivity of 444.3 kPa-1 and a broad linear detection range (up to 81.6 kPa). With the help of electrostatic attraction of chitosan and interface locking of PDMS, the pressure sensor achieves remarkable stability of over 100 000 cycles. Simultaneously, the sEMG electrodes offer exceptional stretchability and flexibility, functioning effectively at 60% strain, which ensures precise signal capture for various human motions. After integrating the developed all-in-one arrays into a commercial scoliosis brace, the system can accurately categorize human motion and predict Cobb angles aided by deep learning. This study provides real-time insights into brace effectiveness and patient progress, offering new ideas for improving the efficiency of scoliosis treatment.

Adolescent idiopathic scoliosis (AIS) is a common disease that, in many cases, can be conservatively treated through bracing. High adherence to brace prescription is fundamental to gaining the maximum benefit from this treatment approach. Wearable sensors are available that objectively monitor the brace-wearing time, but their use, combined with other interventions, is poorly investigated. The aims of the current review are as follows: (i) to summarize the real compliance with bracing reported by studies using sensors; (ii) to find out the real brace wearing rate through objective electronic monitoring; (iii) to verify if interventions made to increase adherence to bracing can be effective according to the published literature. We conducted a systematic review of the literature published on Medline, EMBASE, CINAHL, Scopus, CENTRAL, and Web of Science. We identified 466 articles and included examples articles, which had a low to good methodological quality. We found that compliance a greatly varied between 21.8 and 93.9% (weighted average: 58.8%), real brace wearing time varied between 5.7 and 21 h per day (weighted average 13.3), and specific interventions seemed to improve both outcomes, with compliance increasing from 58.5 to 66% and brace wearing increasing from 11.9 to 15.1 h per day. Two comparative studies showed positive effects of stand-alone counseling and information on the sensors' presence when added to counseling. Sensors proved to be useful tools for objectively and continuously monitoring adherence to therapy in everyday clinical practice. Specific interventions, like the use of sensors, counseling, education, and exercises, could increase compliance. However, further studies using high-quality designs should be conducted in this field.

Orthosis-wearing compliance is crucial for achieving positive treatment outcomes in patients with adolescent idiopathic scoliosis (AIS), for whom 23 h of daily wear is typically prescribed. However, self-reported compliance is subjective and often based on patients' memory, leading to inaccuracies. While portable electronic devices have been developed to objectively monitor compliance, relying solely on temperature or force data can be insufficient. This study introduced a novel method that integrated both force and temperature data to estimate orthosis-wearing compliance. Twelve patients (eight females and four males) diagnosed with moderate AIS were included. Each patient was prescribed a thoracic-lumbar-sacral orthosis equipped with an integrated force and temperature sensor system. After one month of orthotic treatment, self-reported wear time averaged 17.8 ± 6.2 h/day, while the sensor indicated an average wear time of 13.3 ± 5.0 h/day. Most patients overestimated their compliance. Nighttime was the most common period for orthosis wear (6.1 h/day), whereas compliance during school hours (2.8 h/day) and after-school hours (3.7 h/day) was lower. The integration of force and temperature sensors provides a more comprehensive understanding of orthosis compliance. Future studies with larger samples and longer monitoring periods are needed to investigate the correlation between compliance and treatment outcomes.

Bracing is a widely used conservative treatment for adolescent idiopathic scoliosis (AIS) patients, yet there is no consensus on the optimal amount of force applied. Although a number of different sensors have been developed to continuously monitor the applied pressure and force, they have several limitations, including inadequate overall force distribution and displacement. They also cause discomfort with limited wearability. In this study, body pressure mapping knitwear (BPMK) integrated with fourteen silicone-embedded fiber Bragg grating (FBG) sensors is developed to monitor immediate and overall changes in force during the bracing treatment. A wear trial of the BPMK is conducted by using a validated soft AIS mannequin, and prediction equations have been formulated for the FBG sensors at individual locations. The findings indicate that the measured forces are in good agreement with those obtained from clinical studies, with peak forces around the padding regions reaching approximately 2N. This was further validated by using finite element (FE) models. When comparing X-ray images, the estimated differences in Cobb angles were found to be 0.6° for the thoracic region and 2.1° for the lumbar region. This model is expected to provide valuable insights into optimal force application, thus minimizing the risk of injury and enhancing bracing compliance and efficacy. Ultimately, this innovative approach provides clinicians with data-driven insights for safer and more effective bracing applications, thus improving the quality of life of AIS patients.

Background: Adolescent idiopathic scoliosis (AIS) affects 2%-3% of adolescents, with conservative treatments like bracing and physiotherapeutic scoliosis-specific exercises (PSSEs) recommended for mild to moderate cases. However, patient compliance with these treatments is often low. Digital tools, including smartphone apps and web applications, offer capabilities such as spinal curvature monitoring, remote consultation, and reduction of health care professionals' workload. These tools may also enhance adherence by increasing motivation and providing real-time feedback, which can be particularly beneficial for patients struggling with brace discomfort or self-esteem concerns. Despite these potential benefits, research remains limited on how digital health technologies can specifically enhance conservative AIS treatment and improve patient outcomes.

Objective: This study aimed to explore the perspectives of multiple stakeholders, including patients, caregivers, and health care providers, on using digital health technologies to improve AIS treatment adherence and outcomes.

Methods: This qualitative research study was conducted in Norway and included 17 participants (1 medical doctor, 2 physiotherapists, 8 patients, 4 family caregivers, and 2 IT specialists). The study adhered to Norwegian regulations. After approval from authorities and approval of the study protocol, patients were recruited through the Norwegian Spine and Back Pain Organization. A portion of the sample was recruited through direct communication from one of the researchers. After obtaining written informed consent from all participants, 5 focus group interviews were conducted between April and June 2023. Data were transcribed, translated, and analyzed using a content analysis approach, with findings reviewed by 2 independent researchers for validation.

Results: The content analysis revealed four key themes: (1) AIS-specific education and information, (2) psychosocial support for patients with AIS and community connection, (3) health care communication and access, and (4) treatment adherence to AIS and gamification. Participants highlighted the need for accessible, adolescent-friendly, and multilingual education on AIS; digital platforms for peer support; improved remote communication with health care providers; and gamification elements tailored to AIS challenges (eg, brace compliance tracking, avatar customization for self-expression, and real-time exercise feedback).

Conclusions: Key findings highlighted the need for accessible information, peer support, and better communication with health care providers, with gamification enhancing treatment adherence. The findings of this study show the potential of digital health technologies in AIS management through fostering accessible information, peer support, and improved communication with health care providers. Customized gamification features may further enhance adherence, offering actionable insights for clinical practice and future research.

Scoliosis is curvature of the spine, often found in adolescents, which can impact on quality of life. Generally, scoliosis is diagnosed by measuring the Cobb angle, which represents the gold standard for scoliosis grade quantification. Commonly, scoliosis evaluation is conducted in person by medical professionals using traditional methods (i.e., involving a scoliometer and/or X-ray radiographs). In recent years, as has happened in various medicine disciplines, it is possible also in orthopedics to observe the spread of Information and Communications Technology (ICT) solutions (i.e., software-based approaches). As an example, smartphone applications (apps) and web-based applications may help the doctors in screening and monitoring scoliosis, thereby reducing the number of in-person visits. This paper aims to provide an overview of the main features of the most popular scoliosis ICT tools, i.e., apps and web-based applications for scoliosis diagnosis, screening, and monitoring. Several apps are assessed and compared with the aim of providing a valid starting point for doctors and patients in their choice of software-based tools. Benefits for the patients may be: reducing the number of visits to the doctor, self-monitoring of scoliosis. Benefits for the doctors may be: monitoring the scoliosis progression over time, managing several patients in a remote way, mining the data of several patients for evaluating different therapeutic or exercise prescriptions. We first propose a methodology for the evaluation of scoliosis apps in which five macro-categories are considered: (i) technological aspects (e.g., available sensors, how angles are measured); (ii) the type of measurements (e.g., Cobb angle, angle of trunk rotation, axial vertebral rotation); (iii) availability (e.g., app store and eventual fee to pay); (iv) the functions offered to the user (e.g., posture monitoring, exercise prescription); (v) overall evaluation (e.g., pros and cons, usability). Then, six apps and one web-based application are described and evaluated using this methodology. The results for assessment of scoliosis apps are shown in a tabular format for ease of understanding and intuitive comparison, which can help the doctors, specialists, and families in their choice of scoliosis apps. The use of ICT solutions for spinal curvature assessment and monitoring brings several advantages to both patients and orthopedics specialists. Six scoliosis apps and one web-based application are evaluated, and a guideline for their selection is provided.

Digital interventions hold significant potential in overcoming these challenges. However, few studies have explored the effectiveness of digital PSSE programs and daily posture management for patients with AIS.

The Healbone Intelligent Rehabilitation System (HIRS) is a user-centered smartphone application that integrates remote supervision and guidance of PSSE training with scoliosis-related educational videos and articles to complete a long-term PSSE program and daily self-management. The purpose of this randomized clinical trial was to compare the treatment outcomes of a digital care (DC) group receiving PSSE supervision and guidance through the HIRS and educational videos with those of a usual care group following the traditional PSSE model.

Scoliosis is a three-dimensional deformity of lateral bending and rotation of the spine. Artificial intelligence (AI) is a set of theories and techniques for studying artificial intelligence, which realizes machine intelligence by simulating and expanding human intelligence. With the continuous development of the multidisciplinary integration trend of modern medicine, artificial intelligence is used more and more in the diagnosis and treatment of scoliosis. Artificial intelligence has been widely used in the study of scoliosis and has penetrated into all fields of clinical practice of scoliosis. At present, artificial intelligence has shown good application prospects in early screening, diagnosis, treatment decision making, intraoperative operation, and prognosis prediction of scoliosis. This paper mainly summarizes the application of artificial intelligence in the clinical practice of scoliosis, and briefly introduces the AI model and its specific applications. In addition, this paper also discusses the limitations and future development of artificial intelligence. In the future, artificial intelligence will bring greater convenience to the diagnosis and treatment of scoliosis and provide better therapeutic effects for patients.

Scoliosis is a three-dimensional deformity of lateral bending and rotation of the spine. Artificial intelligence (AI) is a set of theories and techniques for studying artificial intelligence, which realizes machine intelligence by simulating and expanding human intelligence. With the continuous development of the multidisciplinary integration trend of modern medicine, artificial intelligence is used more and more in the diagnosis and treatment of scoliosis. Artificial intelligence has been widely used in the study of scoliosis and has penetrated into all fields of clinical practice of scoliosis. At present, artificial intelligence has shown good application prospects in early screening, diagnosis, treatment decision making, intraoperative operation, and prognosis prediction of scoliosis. This paper mainly summarizes the application of artificial intelligence in the clinical practice of scoliosis, and briefly introduces the AI model and its specific applications. In addition, this paper also discusses the limitations and future development of artificial intelligence. In the future, artificial intelligence will bring greater convenience to the diagnosis and treatment of scoliosis and provide better therapeutic effects for patients.

Purpose: The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefore, the current review sought to synthesize findings from the literature at the interface of artificial intelligence and spine research.

Methods: A narrative review was performed based on the literature of three databases (MEDLINE, CINAHL, and Scopus) from January 2015 to March 2021 that examined historical and recent advancements in the understanding of artificial intelligence and machine learning in spine research. Studies were appraised for their role in, or description of, advancements within image recognition and predictive modeling for spinal research. Only English articles that fulfilled inclusion criteria were ultimately incorporated in this review.

Results: This review briefly summarizes the history and applications of artificial intelligence and machine learning in spine. Three basic machine learning training paradigms: supervised learning, unsupervised learning, and reinforced learning are also discussed. Artificial intelligence and machine learning have been utilized in almost every facet of spine ranging from localization and segmentation techniques in spinal imaging to pathology specific algorithms which include but not limited to; preoperative risk assessment of postoperative complications, screening algorithms for patients at risk of osteoporosis and clustering analysis to identify subgroups within adolescent idiopathic scoliosis. The future of artificial intelligence and machine learning in spine surgery is also discussed with focusing on novel algorithms, data collection techniques and increased utilization of automated systems.

Conclusion: Improvements to modern-day computing and accessibility to various imaging modalities allow for innovative discoveries that may arise, for example, from management. Given the imminent future of AI in spine surgery, it is of great importance that practitioners continue to inform themselves regarding AI, its goals, use, and progression. In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.

Artificial intelligence (AI) has transformed healthcare, particularly in robot-assisted surgery, rehabilitation, medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and adherence, and administrative applications. AI enhances pre-operative planning, intraoperative guidance, and post-operative outcomes in robotic surgery. In rehabilitation, AI enables personalized programs, physical therapy using robotics, and in real time monitoring and feedback mechanisms. The integration of AI with emerging technologies like augmented reality, virtual reality, and the Internet of Things holds promise for broader healthcare applications. However, AI adoption faces technical challenges related to data quality and bias, ethical and privacy concerns, regulatory and legal considerations, and issues of cost and accessibility. Future trends include advances in AI algorithms and robotics, integration with emerging technologies, and the potential for wider applications in healthcare and rehabilitation. Addressing ethical and security considerations is crucial for the successful integration of AI in healthcare while upholding patient safety and legal standards. Overcoming regulatory, ethical, and trust-based challenges with effective governance will be critical to the full realization of AI potential in healthcare artificial intelligence (AI)-driven healthcare solutions powered by IoT can enable in real time patient monitoring, enhancing early diagnosis and chronic illness management. AI applications in AR/VR can transform medical education by allowing healthcare professionals to practice intricate procedures in a safe environment. Wearable technology with AI-driven analytics can offer personalized health insights, facilitating proactive interventions and improved patient outcomes. Adopting these innovations can foster progress, enhance patient care, and boost overall healthcare efficiency. Future studies should refine these cross-disciplinary applications, ensure their smooth incorporation into current healthcare systems, and tackle potential ethical and security issues.

Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral components of modern healthcare, offering new avenues for diagnosis, treatment, and outcome prediction. This review explores their current applications and potential future in the field of spinal care. From enhancing imaging techniques to predicting patient outcomes, AI and ML are revolutionizing the way we approach spinal diseases. AI and ML have significantly improved spinal imaging by augmenting detection and classification capabilities, thereby boosting diagnostic accuracy. Predictive models have also been developed to guide treatment plans and foresee patient outcomes, driving a shift towards more personalized care. Looking towards the future, we envision AI and ML further ingraining themselves in spinal care with the development of algorithms capable of deciphering complex spinal pathologies to aid decision making. Despite the promise these technologies hold, their integration into clinical practice is not without challenges. Data quality, integration hurdles, data security, and ethical considerations are some of the key areas that need to be addressed for their successful and responsible implementation. In conclusion, AI and ML represent potent tools for transforming spinal care. Thoughtful and balanced integration of these technologies, guided by ethical considerations, can lead to significant advancements, ushering in an era of more personalized, effective, and efficient healthcare.

The integration of artificial intelligence (AI) technologies into physical and mental rehabilitation has the potential to significantly transform these fields. AI innovations, including machine learning algorithms, natural language processing, and computer vision, offer occupational therapists advanced tools to improve care quality. These technologies facilitate more precise assessments, the development of tailored intervention plans, more efficient treatment delivery, and enhanced outcome evaluation. This review explores the integration of AI across various aspects of rehabilitation, providing a thorough examination of recent advancements and current applications. It highlights how AI applications, such as natural language processing, computer vision, virtual reality, machine learning, and robotics, are shaping the future of physical and mental recovery in occupational therapy.

Artificial intelligence (AI), with its technologies such as machine perception, robotics, natural language processing, expert systems, and machine learning (ML) with its subset deep learning, have transformed patient care and administration in all fields of modern medicine. For many clinicians, however, the nature, scope, and resulting possibilities of ML and deep learning might not yet be fully clear. This narrative review provides an overview of the application of ML and deep learning in musculoskeletal medicine. It first introduces the concept of AI and machine learning and its associated fields. Different machine concepts such as supervised, unsupervised and reinforcement learning will then be presented with current applications and clinical perspective. Finally deep learning applications will be discussed. With significant improvements over the last decade, ML and its subset deep learning today offer potent tools for numerous applications to implement in clinical practice. While initial setup costs are high, these investments can reduce workload and cost globally. At the same time, many challenges remain, such as standardisation in data labelling and often insufficient validity of the obtained results. In addition, legal aspects still will have to be clarified. Until good analyses and predictions are obtained by an ML tool, patience in training and suitable data sets are required. Awareness of the strengths of ML and the limitations that lie within it will help put this technique to good use.

The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study.

Introduction: Expanding the use of surface electromyography-biofeedback (EMG-BF) devices in different therapeutic settings highlights the gradually evolving role of visualizing muscle activity in the rehabilitation process. This review evaluates their concepts, uses, and trends, combining evidence-based research.

Areas Covered: This review dissects the anatomy of EMG-BF systems, emphasizing their transformative integration with machine-learning (ML) and deep-learning (DL) paradigms. Advances such as the application of sophisticated DL architectures for high-density EMG data interpretation, optimization techniques for heightened DL model performance, and the fusion of EMG with electroencephalogram (EEG) signals have been spotlighted for enhancing biomechanical analyses in rehabilitation. The literature survey also categorizes EMG-BF devices based on functionality and clinical usage, supported by insights from commercial sectors.

Expert Opinion: The current landscape of EMG-BF is rapidly evolving, chiefly propelled by innovations in artificial intelligence (AI). The incorporation of ML and DL into EMG-BF systems augments their accuracy, reliability, and scope, marking a leap in patient care. Despite challenges in model interpretability and signal noise, ongoing research promises to address these complexities, refining biofeedback modalities. The integration of AI not only predicts patient-specific recovery timelines but also tailors therapeutic interventions, heralding a new era of personalized medicine in rehabilitation and emotional detection.

Background Context: Healthcare reforms that demand quantitative outcomes and technical innovations have emphasized the use of Disability and Functional Outcome Measurements (DFOMs) to spinal conditions and interventions. Virtual healthcare has become increasingly important following the COVID-19 pandemic and wearable medical devices have proven to be a useful adjunct. Thus, given the advancement of wearable technology, broad adoption of commercial devices (ie, smartwatches, phone applications, and wearable monitors) by the general public, and the growing demand from consumers to take control of their health, the medical industry is now primed to formally incorporate evidence-based wearable device-mediated telehealth into standards of care.

Purpose: To (1) identify all wearable devices in the peer-reviewed literature that were used to assess DFOMs in Spine, (2) analyze clinical studies implementing such devices in spine care, and (3) provide clinical commentary on how such devices might be integrated into standards of care.

Study Design/setting: A systematic review.

Methods: A comprehensive systematic review was conducted in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) across the following databases: PubMed; MEDLINE; EMBASE (Elsevier); and Scopus. Articles related to wearables systems in spine healthcare were selected. Extracted data was collected as per a predetermined checklist including wearable device type, study design, and clinical indices studied.

Results: Of the 2,646 publications that were initially screened, 55 were extensively analyzed and selected for retrieval. Ultimately 39 publications were identified as being suitable for inclusion based on the relevance of their content to the core objectives of this systematic review. The most relevant studies were included, with a focus on wearables technologies that can be used in patients' home environments.

Conclusions: Wearable technologies mentioned in this paper have the potential to revolutionize spine healthcare through their ability to collect data continuously and in any environment. In this paper, the vast majority of wearable spine devices rely exclusively on accelerometers. Thus, these metrics provide information about general health rather than specific impairments caused by spinal conditions. As wearable technology becomes more prevalent in orthopedics, healthcare costs may be reduced and patient outcomes will improve. A combination of DFOMs gathered using a wearable device in conjunction with patient-reported outcomes and radiographic measurements will provide a comprehensive evaluation of a spine patient's health and assist the physician with patient-specific treatment decision-making. Establishing these ubiquitous diagnostic capabilities will allow improvement in patient monitoring and help us learn about postoperative recovery and the impact of our interventions.

Due to the growth of sensor technology, more affordable integrated circuits, and connectivity technologies, the usage of wearable equipment and sensing devices for monitoring physical activities, whether for wellness, sports monitoring, or medical rehabilitation, has exploded. The current literature review was performed between October 2022 and February 2023 using PubMed, Web of Science, and Scopus in accordance with P.R.I.S.M.A. criteria. The screening phase resulted in the exclusion of 69 articles that did not fit the themes developed in all subchapters of the study, 41 articles that dealt exclusively with rehabilitation and orthopaedics, 28 articles whose abstracts were not visible, and 10 articles that dealt exclusively with other sensor-based devices and not medical ones; the inclusion phase resulted in the inclusion of 111 articles. Patients who utilise sensor-based devices have several advantages due to rehabilitating a missing component, which marks the accomplishment of a fundamental goal within the rehabilitation program. As technology moves faster and faster forward, the field of medical rehabilitation has to adapt to the time we live in by using technology and intelligent devices. This means changing every part of rehabilitation and finding the most valuable and helpful gadgets that can be used to regain lost functions, keep people healthy, or prevent diseases.

Background: Physical disabilities become more common with advancing age. Rehabilitation restores function, maintaining independence for longer. However, the poor availability and accessibility of rehabilitation limits its clinical impact. Artificial Intelligence (AI) guided interventions have improved many domains of healthcare, but whether rehabilitation can benefit from AI remains unclear.

Methods: We conducted a systematic review of AI-supported physical rehabilitation technology tested in the clinical setting to understand: 1) availability of AI-supported physical rehabilitation technology; 2) its clinical effect; 3) and the barriers and facilitators to implementation. We searched in MEDLINE, EMBASE, CINAHL, Science Citation Index (Web of Science), CIRRIE (now NARIC), and OpenGrey.

Results: We identified 9054 articles and included 28 projects. AI solutions spanned five categories: App-based systems, robotic devices that replace function, robotic devices that restore function, gaming systems and wearables. We identified five randomised controlled trials (RCTs), which evaluated outcomes relating to physical function, activity, pain, and health-related quality of life. The clinical effects were inconsistent. Implementation barriers included technology literacy, reliability, and user fatigue. Enablers included greater access to rehabilitation programmes, remote monitoring of progress, reduction in manpower requirements and lower cost.

Conclusion: Application of AI in physical rehabilitation is a growing field, but clinical effects have yet to be studied rigorously. Developers must strive to conduct robust clinical evaluations in the real-world setting and appraise post implementation experiences.

Artificial intelligence (AI) is a broad term referring to the application of computational algorithms that can analyze large data sets to classify, predict, or gain useful conclusions. Under the umbrella of AI is machine learning (ML). ML is the process of building or learning statistical models using previously observed real world data to predict outcomes, or categorize observations based on 'training' provided by humans. These predictions are then applied to future data, all the while folding in the new data into its perpetually improving and calibrated statistical model. The future of AI and ML in healthcare research is exciting and expansive. AI and ML are becoming cornerstones in the medical and healthcare-research domains and are integral in our continued processing and capitalization of robust patient EMR data. Considerations for the use and application of ML in healthcare settings include assessing the quality of data inputs and decision-making that serve as the foundations of the ML model, ensuring the end-product is interpretable, transparent, and ethical concerns are considered throughout the development process. The current and future applications of ML include improving the quality and quantity of data collected from EMRs to improve registry data, utilizing these robust datasets to improve and standardized research protocols and outcomes, clinical decision-making applications, natural language processing and improving the fundamentals of value-based care, to name only a few.

With the advent of artificial intelligence (AI), machines are increasingly being used to complete complicated tasks, yielding remarkable results. Machine learning (ML) is the most relevant subset of AI in medicine, which will soon become an integral part of our everyday practice. Therefore, physicians should acquaint themselves with ML and AI, and their role as an enabler rather than a competitor. Herein, we introduce basic concepts and terms used in AI and ML, and aim to demystify commonly used AI/ML algorithms such as learning methods including neural networks/deep learning, decision tree and application domain in computer vision and natural language processing through specific examples. We discuss how machines are already being used to augment the physician's decision-making process, and postulate the potential impact of ML on medical practice and medical research based on its current capabilities and known limitations. Moreover, we discuss the feasibility of full machine autonomy in medicine.

Artificial intelligence (AI) and machine learning refer to computers built and programed by humans to perform tasks according to our design. This is vital to keep in mind as we try to understand the application of AI to medicine. AI is a tool with strengths and limitations. The primary strength of AI is that it allows us to assimilate and process unlimited quantities of health care data. The limits of AI include the inability of machines to adapt in a human sense, the reality that machines lack human insight (i.e., clinical judgment or common sense), and the limitation that machine-learning algorithms are subject to the data on which they are trained. Thus, we must adapt to AI and machine learning. Next, because machine learning is a type of AI in which computers are programmed to improve the algorithms under which they function over time, we require insight to achieve an element of explainability about the key data underlining a particular machine-learning prediction. Finally, machine-learning algorithms require validation before they can be applied to data sets different from the data on which they were trained. As computers have become faster and more powerful, and as the availability of digital data has become immense, we can program our machines to analyze data and recognize patterns that, in sum, are a primary basis of medical diagnosis and treatment.

Artificial intelligence (AI), with its technologies such as machine perception, robotics, natural language processing, expert systems, and machine learning (ML) with its subset deep learning, have transformed patient care and administration in all fields of modern medicine. For many clinicians, however, the nature, scope, and resulting possibilities of ML and deep learning might not yet be fully clear. This narrative review provides an overview of the application of ML and deep learning in musculoskeletal medicine. It first introduces the concept of AI and machine learning and its associated fields. Different machine concepts such as supervised, unsupervised and reinforcement learning will then be presented with current applications and clinical perspective. Finally deep learning applications will be discussed. With significant improvements over the last decade, ML and its subset deep learning today offer potent tools for numerous applications to implement in clinical practice. While initial setup costs are high, these investments can reduce workload and cost globally. At the same time, many challenges remain, such as standardisation in data labelling and often insufficient validity of the obtained results. In addition, legal aspects still will have to be clarified. Until good analyses and predictions are obtained by an ML tool, patience in training and suitable data sets are required. Awareness of the strengths of ML and the limitations that lie within it will help put this technique to good use.

In recent decades, artificial intelligence (AI) has infiltrated a variety of domains, including media, education, and medicine. There exists no glossary, lexicon, or reference for the uninitiated medical professional to explore the new terminology. As AI-driven technologies and applications become more available for clinical use in healthcare settings, an understanding of basic components, models, and tasks related to AI is crucial for clinical and academic appraisal. Here, we present a glossary of AI definitions that healthcare professionals can utilize to augment personal understanding of AI during this fourth industrial revolution.

Level Of Evidence: Level V, expert opinion.

Stroke is a leading cause of long-term disability worldwide. With the advancements in sensor technologies and data availability, artificial intelligence (AI) holds the promise of improving the amount, quality and efficiency of care and enhancing the precision of stroke rehabilitation. We aimed to identify and characterize the existing research on AI applications in stroke recovery and rehabilitation of adults, including categories of application and progression of technologies over time. Data were collected from peer-reviewed articles across various electronic databases up to January 2024. Insights were extracted using AI-enhanced multi-method, data-driven techniques, including clustering of themes and topics. This scoping review summarizes outcomes from 704 studies. Four common themes (impairment, assisted intervention, prediction and imaging, and neuroscience) were identified, in which time-linked patterns emerged. The impairment theme revealed a focus on motor function, gait and mobility, while the assisted intervention theme included applications of robotic and brain-computer interface (BCI) techniques. AI applications progressed over time, starting from conceptualization and then expanding to a broader range of techniques in supervised learning, artificial neural networks (ANN), natural language processing (NLP) and more. Applications focused on upper limb rehabilitation were reviewed in more detail, with machine learning (ML), deep learning techniques and sensors such as inertial measurement units (IMU) used for upper limb and functional movement analysis. AI applications have potential to facilitate tailored therapeutic delivery, thereby contributing to the optimization of rehabilitation outcomes and promoting sustained recovery from rehabilitation to real-world settings.

Artificial intelligence methods are being applied broadly in society and increasingly in health care and research. Machine learning, a subset of artificial intelligence, represents the study of algorithms that improve automatically with experience. This article provides a basic overview of artificial intelligence, machine learning categories, common applications in the business sphere, advantages and disadvantages of using this technology, and example applications in rehabilitation and other fields for contextual purposes. The study and implementation of machine learning and artificial intelligence can function to improve patient care and represents a burgeoning area of research.

The technology at the heart of the most innovative progress in health care artificial intelligence (AI) is in a subdomain called machine learning (ML), which describes the use of software algorithms to identify patterns in very large datasets. ML has driven much of the progress of health care AI over the past 5 years, demonstrating impressive results in clinical decision support, patient monitoring and coaching, surgical assistance, patient care, and systems management. Clinicians in the near future will find themselves working with information networks on a scale well beyond the capacity of human beings to grasp, thereby necessitating the use of intelligent machines to analyze and interpret the complex interactions between data, patients, and clinical decision makers. However, as this technology becomes more powerful, it also becomes less transparent, and algorithmic decisions are therefore progressively more opaque. This is problematic because computers will increasingly be asked for answers to clinical questions that have no single right answer and that are open-ended, subjective, and value laden. As ML continues to make important contributions in a variety of clinical domains, clinicians will need to have a deeper understanding of the design, implementation, and evaluation of ML to ensure that current health care is not overly influenced by the agenda of technology entrepreneurs and venture capitalists. The aim of this article is to provide a nontechnical introduction to the concept of ML in the context of health care, the challenges that arise, and the resulting implications for clinicians.

Artificial intelligence (AI), including machine learning (ML), has transformed numerous industries through newfound efficiencies and supportive decision-making. With the exponential growth of computing power and large datasets, AI has transitioned from theory to reality in teaching machines to automate tasks without human supervision. AI-based computational algorithms analyze "training sets" using pattern recognition and learning from inputted data to classify and predict outputs that otherwise could not be effectively analyzed with human processing or standard statistical methods. Though widespread understanding of the fundamental principles and adoption of applications have yet to be achieved, recent applications and research efforts implementing AI have demonstrated great promise in predicting future injury risk, interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting telehealth. With appreciation, caution, and experience applying AI, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. The purpose of this review is to discuss the pearls, pitfalls, and applications associated with AI.

With increasing computing power, artificial intelligence (AI) and machine learning (ML) have prospered, which facilitate the analysis of large datasets, especially those found in critical care. It is important to define these terminologies, to inform a standardized approach to critical care research. This manuscript hopes to clarify these terms with examples from medical literature. Three major components that are required for a successful ML implementation: (i) reliable dataset, (ii) ML algorithm, and (iii) unbiased model evaluation, are discussed. A reliable dataset can be structured or unstructured with limited noise, outliers, and missing values. ML, a subset of AI, is typically focused on supervised or unsupervised learning tasks in which the output is based on inputs and derived from iterative pattern recognition algorithms, while AI is the overall ability of a machine to "think" or mimic human behavior; and to analyze data free from human influence. Even with successful implementation, advanced AI and ML algorithms have faced challenges in adoption into practice, mainly due to their lack of interpretability, which hinders trust, buy-in, and engagement from clinicians. Consequently, traditional algorithms, such as linear and logistic regression, that may have reduced predictive power but are highly interpretable, continue to be widely used.

In the 1990s and into the early 2000s, even with slow computers and limited memory, the problem of having machines successfully perform certain medical tasks that were repetitive, and therefore prone to human error, was being solved. Through a substantial investment of money and intellectual effort, computer reading of electrocardiograms (ECGs) and white-cell differential counts, analysis of retinal photographs and cutaneous lesions, and other image-processing tasks has become a reality. Many of these machine-learning–aided tasks have been largely accepted and incorporated into the everyday practice of medicine. The performance of these machine tasks is not perfect and often requires a skilled person to oversee the process, but in many cases, it is good enough, given the need for relatively rapid interpretation of images and the lack of local expertise.

However, the use of AI and machine learning in medicine has expanded beyond the reading of medical images. AI and machine-learning programs have entered medicine in many ways, including, but not limited to, helping to identify outbreaks of infectious diseases that may have an impact on public health; combining clinical, genetic, and many other laboratory outputs to identify rare and common conditions that might otherwise have escaped detection; and aiding in hospital business operations (Figure 2). In the months to come, the Journal will publish other review articles that take a selective look at AI and machine learning in medicine in 2023. But before the first article appears, in about a month’s time, it is important to consider the overriding issues that need to be considered as we learn to work hand in hand with machines.

Figure 2:
Caption: Spectrum of Artificial Intelligence (AI) in Medicine.
Description: Panel A shows selected areas of public health and medicine in which AI has an established but evolving role. These tools are already helping medical professionals do their jobs as partners in practice. EMR denotes electronic medical record. Panel B shows areas of medical practice in which AI has begun to have an influence but has not yet reached the stage of common use.

Artificial intelligence (AI) is a broad term referring to the application of computational algorithms that can analyze large data sets to classify, predict, or gain useful conclusions. Under the umbrella of AI is machine learning (ML). ML is the process of building or learning statistical models using previously observed real world data to predict outcomes, or categorize observations based on 'training' provided by humans. These predictions are then applied to future data, all the while folding in the new data into its perpetually improving and calibrated statistical model. The future of AI and ML in healthcare research is exciting and expansive. AI and ML are becoming cornerstones in the medical and healthcare-research domains and are integral in our continued processing and capitalization of robust patient EMR data. Considerations for the use and application of ML in healthcare settings include assessing the quality of data inputs and decision-making that serve as the foundations of the ML model, ensuring the end-product is interpretable, transparent, and ethical concerns are considered throughout the development process. The current and future applications of ML include improving the quality and quantity of data collected from EMRs to improve registry data, utilizing these robust datasets to improve and standardized research protocols and outcomes, clinical decision-making applications, natural language processing and improving the fundamentals of value-based care, to name only a few.

In recent decades, artificial intelligence (AI) has infiltrated a variety of domains, including media, education, and medicine. There exists no glossary, lexicon, or reference for the uninitiated medical professional to explore the new terminology. As AI-driven technologies and applications become more available for clinical use in healthcare settings, an understanding of basic components, models, and tasks related to AI is crucial for clinical and academic appraisal. Here, we present a glossary of AI definitions that healthcare professionals can utilize to augment personal understanding of AI during this fourth industrial revolution.

Level Of Evidence: Level V, expert opinion.

The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.

The integration of artificial intelligence (AI) technologies into physical and mental rehabilitation has the potential to significantly transform these fields. AI innovations, including machine learning algorithms, natural language processing, and computer vision, offer occupational therapists advanced tools to improve care quality. These technologies facilitate more precise assessments, the development of tailored intervention plans, more efficient treatment delivery, and enhanced outcome evaluation. This review explores the integration of AI across various aspects of rehabilitation, providing a thorough examination of recent advancements and current applications. It highlights how AI applications, such as natural language processing, computer vision, virtual reality, machine learning, and robotics, are shaping the future of physical and mental recovery in occupational therapy.

Artificial intelligence (AI) has transformed healthcare, particularly in robot-assisted surgery, rehabilitation, medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and adherence, and administrative applications. AI enhances pre-operative planning, intraoperative guidance, and post-operative outcomes in robotic surgery. In rehabilitation, AI enables personalized programs, physical therapy using robotics, and in real time monitoring and feedback mechanisms. The integration of AI with emerging technologies like augmented reality, virtual reality, and the Internet of Things holds promise for broader healthcare applications. However, AI adoption faces technical challenges related to data quality and bias, ethical and privacy concerns, regulatory and legal considerations, and issues of cost and accessibility. Future trends include advances in AI algorithms and robotics, integration with emerging technologies, and the potential for wider applications in healthcare and rehabilitation. Addressing ethical and security considerations is crucial for the successful integration of AI in healthcare while upholding patient safety and legal standards. Overcoming regulatory, ethical, and trust-based challenges with effective governance will be critical to the full realization of AI potential in healthcare artificial intelligence (AI)-driven healthcare solutions powered by IoT can enable in real time patient monitoring, enhancing early diagnosis and chronic illness management. AI applications in AR/VR can transform medical education by allowing healthcare professionals to practice intricate procedures in a safe environment. Wearable technology with AI-driven analytics can offer personalized health insights, facilitating proactive interventions and improved patient outcomes. Adopting these innovations can foster progress, enhance patient care, and boost overall healthcare efficiency. Future studies should refine these cross-disciplinary applications, ensure their smooth incorporation into current healthcare systems, and tackle potential ethical and security issues.

Stroke is a leading cause of long-term disability worldwide. With the advancements in sensor technologies and data availability, artificial intelligence (AI) holds the promise of improving the amount, quality and efficiency of care and enhancing the precision of stroke rehabilitation. We aimed to identify and characterize the existing research on AI applications in stroke recovery and rehabilitation of adults, including categories of application and progression of technologies over time. Data were collected from peer-reviewed articles across various electronic databases up to January 2024. Insights were extracted using AI-enhanced multi-method, data-driven techniques, including clustering of themes and topics. This scoping review summarizes outcomes from 704 studies. Four common themes (impairment, assisted intervention, prediction and imaging, and neuroscience) were identified, in which time-linked patterns emerged. The impairment theme revealed a focus on motor function, gait and mobility, while the assisted intervention theme included applications of robotic and brain-computer interface (BCI) techniques. AI applications progressed over time, starting from conceptualization and then expanding to a broader range of techniques in supervised learning, artificial neural networks (ANN), natural language processing (NLP) and more. Applications focused on upper limb rehabilitation were reviewed in more detail, with machine learning (ML), deep learning techniques and sensors such as inertial measurement units (IMU) used for upper limb and functional movement analysis. AI applications have potential to facilitate tailored therapeutic delivery, thereby contributing to the optimization of rehabilitation outcomes and promoting sustained recovery from rehabilitation to real-world settings.

Artificial intelligence (AI) is a broad term referring to the application of computational algorithms that can analyze large data sets to classify, predict, or gain useful conclusions. Under the umbrella of AI is machine learning (ML). ML is the process of building or learning statistical models using previously observed real world data to predict outcomes, or categorize observations based on 'training' provided by humans. These predictions are then applied to future data, all the while folding in the new data into its perpetually improving and calibrated statistical model. The future of AI and ML in healthcare research is exciting and expansive. AI and ML are becoming cornerstones in the medical and healthcare-research domains and are integral in our continued processing and capitalization of robust patient EMR data. Considerations for the use and application of ML in healthcare settings include assessing the quality of data inputs and decision-making that serve as the foundations of the ML model, ensuring the end-product is interpretable, transparent, and ethical concerns are considered throughout the development process. The current and future applications of ML include improving the quality and quantity of data collected from EMRs to improve registry data, utilizing these robust datasets to improve and standardized research protocols and outcomes, clinical decision-making applications, natural language processing and improving the fundamentals of value-based care, to name only a few.

With increasing computing power, artificial intelligence (AI) and machine learning (ML) have prospered, which facilitate the analysis of large datasets, especially those found in critical care. It is important to define these terminologies, to inform a standardized approach to critical care research. This manuscript hopes to clarify these terms with examples from medical literature. Three major components that are required for a successful ML implementation: (i) reliable dataset, (ii) ML algorithm, and (iii) unbiased model evaluation, are discussed. A reliable dataset can be structured or unstructured with limited noise, outliers, and missing values. ML, a subset of AI, is typically focused on supervised or unsupervised learning tasks in which the output is based on inputs and derived from iterative pattern recognition algorithms, while AI is the overall ability of a machine to "think" or mimic human behavior; and to analyze data free from human influence. Even with successful implementation, advanced AI and ML algorithms have faced challenges in adoption into practice, mainly due to their lack of interpretability, which hinders trust, buy-in, and engagement from clinicians. Consequently, traditional algorithms, such as linear and logistic regression, that may have reduced predictive power but are highly interpretable, continue to be widely used.

In recent decades, artificial intelligence (AI) has infiltrated a variety of domains, including media, education, and medicine. There exists no glossary, lexicon, or reference for the uninitiated medical professional to explore the new terminology. As AI-driven technologies and applications become more available for clinical use in healthcare settings, an understanding of basic components, models, and tasks related to AI is crucial for clinical and academic appraisal. Here, we present a glossary of AI definitions that healthcare professionals can utilize to augment personal understanding of AI during this fourth industrial revolution.

Level Of Evidence: Level V, expert opinion.

The integration of artificial intelligence (AI) technologies into physical and mental rehabilitation has the potential to significantly transform these fields. AI innovations, including machine learning algorithms, natural language processing, and computer vision, offer occupational therapists advanced tools to improve care quality. These technologies facilitate more precise assessments, the development of tailored intervention plans, more efficient treatment delivery, and enhanced outcome evaluation. This review explores the integration of AI across various aspects of rehabilitation, providing a thorough examination of recent advancements and current applications. It highlights how AI applications, such as natural language processing, computer vision, virtual reality, machine learning, and robotics, are shaping the future of physical and mental recovery in occupational therapy.

Artificial intelligence methods are being applied broadly in society and increasingly in health care and research. Machine learning, a subset of artificial intelligence, represents the study of algorithms that improve automatically with experience. This article provides a basic overview of artificial intelligence, machine learning categories, common applications in the business sphere, advantages and disadvantages of using this technology, and example applications in rehabilitation and other fields for contextual purposes. The study and implementation of machine learning and artificial intelligence can function to improve patient care and represents a burgeoning area of research.

Artificial intelligence (AI) and machine learning refer to computers built and programed by humans to perform tasks according to our design. This is vital to keep in mind as we try to understand the application of AI to medicine. AI is a tool with strengths and limitations. The primary strength of AI is that it allows us to assimilate and process unlimited quantities of health care data. The limits of AI include the inability of machines to adapt in a human sense, the reality that machines lack human insight (i.e., clinical judgment or common sense), and the limitation that machine-learning algorithms are subject to the data on which they are trained. Thus, we must adapt to AI and machine learning. Next, because machine learning is a type of AI in which computers are programmed to improve the algorithms under which they function over time, we require insight to achieve an element of explainability about the key data underlining a particular machine-learning prediction. Finally, machine-learning algorithms require validation before they can be applied to data sets different from the data on which they were trained. As computers have become faster and more powerful, and as the availability of digital data has become immense, we can program our machines to analyze data and recognize patterns that, in sum, are a primary basis of medical diagnosis and treatment.

This article was presented at the 2017 annual meeting of the American Association of Hip and Knee Surgeons to introduce the members gathered as the audience to the concepts behind artificial intelligence (AI) and the applications that AI can have in the world of health care today. We discuss the origin of AI, progress to machine learning, and then discuss how the limits of machine learning lead data scientists to develop artificial neural networks and deep learning algorithms through biomimicry. We will place all these technologies in the context of practical clinical examples and show how AI can act as a tool to support and amplify human cognitive functions for physicians delivering care to increasingly complex patients. The aim of this article is to provide the reader with a basic understanding of the fundamentals of AI. Its purpose is to demystify this technology for practicing surgeons so they can better understand how and where to apply it.

Stroke is a leading cause of long-term disability worldwide. With the advancements in sensor technologies and data availability, artificial intelligence (AI) holds the promise of improving the amount, quality and efficiency of care and enhancing the precision of stroke rehabilitation. We aimed to identify and characterize the existing research on AI applications in stroke recovery and rehabilitation of adults, including categories of application and progression of technologies over time. Data were collected from peer-reviewed articles across various electronic databases up to January 2024. Insights were extracted using AI-enhanced multi-method, data-driven techniques, including clustering of themes and topics. This scoping review summarizes outcomes from 704 studies. Four common themes (impairment, assisted intervention, prediction and imaging, and neuroscience) were identified, in which time-linked patterns emerged. The impairment theme revealed a focus on motor function, gait and mobility, while the assisted intervention theme included applications of robotic and brain-computer interface (BCI) techniques. AI applications progressed over time, starting from conceptualization and then expanding to a broader range of techniques in supervised learning, artificial neural networks (ANN), natural language processing (NLP) and more. Applications focused on upper limb rehabilitation were reviewed in more detail, with machine learning (ML), deep learning techniques and sensors such as inertial measurement units (IMU) used for upper limb and functional movement analysis. AI applications have potential to facilitate tailored therapeutic delivery, thereby contributing to the optimization of rehabilitation outcomes and promoting sustained recovery from rehabilitation to real-world settings.

Importance Interest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities.

Observations While AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people’s daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model’s behavior. Prompts such as “Write this note for a specialist consultant” and “Write this note for the patient’s mother” will produce markedly different content.

Conclusions and Relevance Foundation models and generative AI represent a major revolution in AI’s capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.

The historical development of artificial intelligence (AI) in healthcare since the 1960s shows a transformation from simple rule-based systems to complex, data-driven approaches. Early applications focused on decision support, while innovative systems use neural networks and machine learning to recognise patterns in large datasets. The integration of AI technologies in medicine has given rise to diverse areas of application, which can be categorized into preventive AI, diagnostic AI, AI-assisted therapeutic, and administrative AI. Preventive AI analyses risk factors to enable early interventions, while diagnostic AI contributes to faster and more accurate diagnoses. AI-assisted therapy supports individualized treatments, such as personalized medication. Administrative AI optimizes processes such as appointment scheduling, resource management and billing.Despite their potential, AI systems face challenges. These include the fragmentation of health data, a lack of standardisation, data protection concerns and algorithmic biases. The establishment of interoperable data infrastructures and the development of ethical guidelines are crucial to overcoming these hurdles. Future trends include the further development of foundation models (large AI models that are based on broad datasets and can be used in a variety of ways), the integration of structured and unstructured data and greater personalisation in medicine. In the long term, AI can improve the quality and efficiency of healthcare. However, this requires close co-operation between research, industry and politics in order to ensure safe and sustainable implementation.

In recent decades, artificial intelligence (AI) has infiltrated a variety of domains, including media, education, and medicine. There exists no glossary, lexicon, or reference for the uninitiated medical professional to explore the new terminology. As AI-driven technologies and applications become more available for clinical use in healthcare settings, an understanding of basic components, models, and tasks related to AI is crucial for clinical and academic appraisal. Here, we present a glossary of AI definitions that healthcare professionals can utilize to augment personal understanding of AI during this fourth industrial revolution.

Level Of Evidence: Level V, expert opinion.

Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 1940s with the first abstract models of intelligent machines. Soon after, in the 1950s and 1960s, machine learning algorithms such as neural networks and decision trees ignited significant enthusiasm. More recent advancements include the refinement of learning algorithms, the development of convolutional neural networks to efficiently analyze images, and methods to synthesize new images. This renewed enthusiasm was also due to the increase in computational power with graphical processing units and the availability of large digital databases to be mined by neural networks. AI soon began to be applied in medicine, first through expert systems designed to support the clinician's decision and later with neural networks for the detection, classification, or segmentation of malignant lesions in medical images. A recent prospective clinical trial demonstrated the non-inferiority of AI alone compared with a double reading by two radiologists on screening mammography. Natural language processing, recurrent neural networks, transformers, and generative models have both improved the capabilities of making an automated reading of medical images and moved AI to new domains, including the text analysis of electronic health records, image self-labeling, and self-reporting. The availability of open-source and free libraries, as well as powerful computing resources, has greatly facilitated the adoption of deep learning by researchers and clinicians. Key concerns surrounding AI in healthcare include the need for clinical trials to demonstrate efficacy, the perception of AI tools as 'black boxes' that require greater interpretability and explainability, and ethical issues related to ensuring fairness and trustworthiness in AI systems. Thanks to its versatility and impressive results, AI is one of the most promising resources for frontier research and applications in medicine, in particular for oncological applications.

Background: Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners.

Objective: In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research.

Methods: Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy.

Results: From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine.

Conclusions: The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.

Artificial intelligence (AI) has transformed healthcare, particularly in robot-assisted surgery, rehabilitation, medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and adherence, and administrative applications. AI enhances pre-operative planning, intraoperative guidance, and post-operative outcomes in robotic surgery. In rehabilitation, AI enables personalized programs, physical therapy using robotics, and in real time monitoring and feedback mechanisms. The integration of AI with emerging technologies like augmented reality, virtual reality, and the Internet of Things holds promise for broader healthcare applications. However, AI adoption faces technical challenges related to data quality and bias, ethical and privacy concerns, regulatory and legal considerations, and issues of cost and accessibility. Future trends include advances in AI algorithms and robotics, integration with emerging technologies, and the potential for wider applications in healthcare and rehabilitation. Addressing ethical and security considerations is crucial for the successful integration of AI in healthcare while upholding patient safety and legal standards. Overcoming regulatory, ethical, and trust-based challenges with effective governance will be critical to the full realization of AI potential in healthcare artificial intelligence (AI)-driven healthcare solutions powered by IoT can enable in real time patient monitoring, enhancing early diagnosis and chronic illness management. AI applications in AR/VR can transform medical education by allowing healthcare professionals to practice intricate procedures in a safe environment. Wearable technology with AI-driven analytics can offer personalized health insights, facilitating proactive interventions and improved patient outcomes. Adopting these innovations can foster progress, enhance patient care, and boost overall healthcare efficiency. Future studies should refine these cross-disciplinary applications, ensure their smooth incorporation into current healthcare systems, and tackle potential ethical and security issues.

AI is broadly defined as a machine or computing platform that is capable of making intelligent decisions. Two types of AI have generally been pursued in health care delivery: machine learning, which involves computational techniques that learn from examples instead of operating from predefined rules, and natural language processing, which is the ability of a computer to transform human language and unstructured text into machine-readable structured data that reliably reflect the intent of the language.

In health care delivery, the role of AI in improving clinical judgment has garnered the most attention, with a particular focus on prognosis, diagnosis, treatment, clinician workflow, and expansion of clinical expertise. Specialties such as radiology, pathology, dermatology, and cardiology are already using AI in the process of image analysis. In radiologic screening, for example, up to 30% of radiology practices that responded to a survey indicated that they had adopted AI by 2020, and another 20% of radiology practices indicated that they planned to begin using AI in the near future.

The potential of AI, however, extends much further. We have found that uses of AI are emerging in nine domains of health care delivery (Figure 1). However, most uses of AI in health care delivery have not been subject to randomized, controlled trials. Therefore, the usual level of evidence required for medical decision making may be lacking. We indicate where there is ample evidence and where it is absent. We still aim to provide a perspective based on our conversations with dozens of health care leaders, but we understand that this is not a substitute for a randomized, controlled trial.

Our discussions with U.S. health care leaders suggest that AI adoption in the nine health care domains has been met with varying degrees of success. Newer forms of technology, such as blockchain and generative AI, have not played a major role. Some health care leaders argue that unlocking the potential of AI will require the use of these types of technology, but our experience suggests otherwise. Overall, most organizations are still in the pilot phase of AI adoption and are attempting to validate the benefits. Here, we focus on uses of AI in three domains of health care delivery: reimbursement, clinical operations, and quality and safety.

Figure 1:
Caption: Examples of the Use of Artificial Intelligence (AI) in Health Care Delivery Domains.
Description: Shown is a breakdown of health care delivery domains, with examples of AI use and the current view of its potential effect on total mission value and the state of adoption. Total mission value includes both financial and nonfinancial factors such as quality improvement, patient safety, patient experience, clinician satisfaction, and increased access to care.

In recent decades, artificial intelligence (AI) has infiltrated a variety of domains, including media, education, and medicine. There exists no glossary, lexicon, or reference for the uninitiated medical professional to explore the new terminology. As AI-driven technologies and applications become more available for clinical use in healthcare settings, an understanding of basic components, models, and tasks related to AI is crucial for clinical and academic appraisal. Here, we present a glossary of AI definitions that healthcare professionals can utilize to augment personal understanding of AI during this fourth industrial revolution.

Level Of Evidence: Level V, expert opinion.

Importance Interest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities.

Observations While AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people’s daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model’s behavior. Prompts such as “Write this note for a specialist consultant” and “Write this note for the patient’s mother” will produce markedly different content.

Conclusions and Relevance Foundation models and generative AI represent a major revolution in AI’s capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.

The historical development of artificial intelligence (AI) in healthcare since the 1960s shows a transformation from simple rule-based systems to complex, data-driven approaches. Early applications focused on decision support, while innovative systems use neural networks and machine learning to recognise patterns in large datasets. The integration of AI technologies in medicine has given rise to diverse areas of application, which can be categorized into preventive AI, diagnostic AI, AI-assisted therapeutic, and administrative AI. Preventive AI analyses risk factors to enable early interventions, while diagnostic AI contributes to faster and more accurate diagnoses. AI-assisted therapy supports individualized treatments, such as personalized medication. Administrative AI optimizes processes such as appointment scheduling, resource management and billing.Despite their potential, AI systems face challenges. These include the fragmentation of health data, a lack of standardisation, data protection concerns and algorithmic biases. The establishment of interoperable data infrastructures and the development of ethical guidelines are crucial to overcoming these hurdles. Future trends include the further development of foundation models (large AI models that are based on broad datasets and can be used in a variety of ways), the integration of structured and unstructured data and greater personalisation in medicine. In the long term, AI can improve the quality and efficiency of healthcare. However, this requires close co-operation between research, industry and politics in order to ensure safe and sustainable implementation.

Artificial Intelligence (AI) and Machine Learning (ML) are powerful tools shaping the healthcare sector. This review considers twelve key aspects of AI in clinical practice: 1) Ethical AI; 2) Explainable AI; 3) Health Equity and Bias in AI; 4) Sponsorship Bias; 5) Data Privacy; 6) Genomics and Privacy; 7) Insufficient Sample Size and Self-Serving Bias; 8) Bridging the Gap Between Training Datasets and Real-World Scenarios; 9) Open Source and Collaborative Development; 10) Dataset Bias and Synthetic Data; 11) Measurement Bias; 12) Reproducibility in AI Research. These categories represent both the challenges and opportunities of AI implementation in healthcare. While AI holds significant potential for improving patient care, it also presents risks and challenges, such as ensuring privacy, combating bias, and maintaining transparency and ethics. The review underscores the necessity of developing comprehensive best practices for healthcare organizations and fostering a diverse dialogue involving data scientists, clinicians, patient advocates, ethicists, economists, and policymakers. We are at the precipice of significant transformation in healthcare powered by AI. By continuing to reassess and refine our approach, we can ensure that AI is implemented responsibly and ethically, maximizing its benefit to patient care and public health.

Recent advances in the science and technology of artificial intelligence (AI) and growing numbers of deployed AI systems in healthcare and other services have called attention to the need for ethical principles and governance. We define and provide a rationale for principles that should guide the commission, creation, implementation, maintenance, and retirement of AI systems as a foundation for governance throughout the lifecycle. Some principles are derived from the familiar requirements of practice and research in medicine and healthcare: beneficence, nonmaleficence, autonomy, and justice come first. A set of principles follow from the creation and engineering of AI systems: explainability of the technology in plain terms; interpretability, that is, plausible reasoning for decisions; fairness and absence of bias; dependability, including "safe failure"; provision of an audit trail for decisions; and active management of the knowledge base to remain up to date and sensitive to any changes in the environment. In organizational terms, the principles require benevolence-aiming to do good through the use of AI; transparency, ensuring that all assumptions and potential conflicts of interest are declared; and accountability, including active oversight of AI systems and management of any risks that may arise. Particular attention is drawn to the case of vulnerable populations, where extreme care must be exercised. Finally, the principles emphasize the need for user education at all levels of engagement with AI and for continuing research into AI and its biomedical and healthcare applications.

Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many technology products and their fields of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks. Various requirements have been raised from different domains, together with numerous tools to debug, justify outcomes, and establish the safety, fairness and reliability of the models. This variety of tasks has led to inconsistencies in the terminology with, for instance, terms such as interpretable, explainable and transparent being often used interchangeably in methodology papers. These words, however, convey different meanings and are "weighted" differently across domains, for example in the technical and social sciences. In this paper, we propose an overarching terminology of interpretability of AI systems that can be referred to by the technical developers as much as by the social sciences community to pursue clarity and efficiency in the definition of regulations for ethical and reliable AI development. We show how our taxonomy and definition of interpretable AI differ from the ones in previous research and how they apply with high versatility to several domains and use cases, proposing a-highly needed-standard for the communication among interdisciplinary areas of AI.

The capabilities of artificial intelligence (AI) have accelerated over the past year, and they are beginning to impact healthcare in a significant way. Could this new technology help address issues that have been difficult and recalcitrant problems for quality and safety for decades? While we are early in the journey, it is clear that we are in the midst of a fundamental shift in AI capabilities. It is also clear these capabilities have direct applicability to healthcare and to improving quality and patient safety, even as they introduce new complexities and risks. Previously, AI focused on one task at a time: for example, telling whether a picture was of a cat or a dog, or whether a retinal photograph showed diabetic retinopathy or not. Foundation models (and their close relatives, generative AI and large language models) represent an important change: they are able to handle many different kinds of problems without additional datasets or training. This review serves as a primer on foundation models' underpinnings, upsides, risks and unknowns-and how these new capabilities may help improve healthcare quality and patient safety.

Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A prime example is ChatGPT, whose capability has compelled people's imagination about the far-reaching influence that large AI models can have and their potential to transform different domains of our lives. In health informatics, the advent of large AI models has brought new paradigms for the design of methodologies. The scale of multi-modal data in the biomedical and health domain has been ever-expanding especially since the community embraced the era of deep learning, which provides the ground to develop, validate, and advance large AI models for breakthroughs in health-related areas. This article presents a comprehensive review of large AI models, from background to their applications. We identify seven key sectors in which large AI models are applicable and might have substantial influence, including: 1) bioinformatics; 2) medical diagnosis; 3) medical imaging; 4) medical informatics; 5) medical education; 6) public health; and 7) medical robotics. We examine their challenges, followed by a critical discussion about potential future directions and pitfalls of large AI models in transforming the field of health informatics.

The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities in medicine. We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI). GMAI models will be capable of carrying out a diverse set of tasks using very little or no task-specific labelled data. Built through self-supervision on large, diverse datasets, GMAI will flexibly interpret different combinations of medical modalities, including data from imaging, electronic health records, laboratory results, genomics, graphs or medical text. Models will in turn produce expressive outputs such as free-text explanations, spoken recommendations or image annotations that demonstrate advanced medical reasoning abilities. Here we identify a set of high-impact potential applications for GMAI and lay out specific technical capabilities and training datasets necessary to enable them. We expect that GMAI-enabled applications will challenge current strategies for regulating and validating AI devices for medicine and will shift practices associated with the collection of large medical datasets.

Scoliosis is one of the most common spinal deformities, which affects millions of people worldwide. Bracing and physiotherapy exercises represent the first-line, non-invasive approaches for managing scoliosis. In recent years, the use of wearable devices has spread as a novel approach to the treatment of scoliosis. However, their effectiveness in treatment planning and outcomes has not been thoroughly evaluated. This manuscript provides a scoping review of the classification and application of wearable devices and the role of artificial intelligence (AI) in interpreting the data collected by wearable devices and guiding the treatment. A systematic search was carried out on Scopus, Web of Science, PubMed, and EMBASE for studies published between January 2020 and February 2025. A total of 269 studies were screened, and 88 articles were reviewed in depth. Inclusion criteria encompassed articles focusing on wearable devices integrated into smart braces, rehabilitation systems for scoliosis management, AI and machine-learning (ML) applications in scoliosis treatment, virtual reality (VR), and telemedicine for scoliosis care. The literature shows that the use of wearable devices can enhance scoliosis treatment by improving the efficiency of braces and enabling remote monitoring in rehabilitation programs. However, more research is needed to evaluate user compliance, long-term effectiveness, and the need for personalized interventions. Future advancements in artificial intelligence, microsensor technology, and data analytics may enhance the efficacy of these devices, which can lead to more personalized and accessible scoliosis treatment.

Stroke is a leading cause of disability worldwide. HandMATE (Hand Movement Assisting Therapy Exoskeleton) addresses accessibility and cost related challenges associated with clinic-based stroke rehabilitation by providing a home-based robotic solution for hand rehabilitation. This study involved 14 chronic stroke patients (10 female, 4 male, average Fugl-Meyer Assessment (FMA) score of 24). Each took home and used a customized HandMATE over a 4-month period. During the first month (Phase 1), subjects had weekly clinic visits for troubleshooting problems and device improvements. During the next 3 months (Phase 2), subjects continued to use the device without weekly clinic visits. Assessments included the FMA, Action Research Arm Test (ARAT), motion capture, and device usage data. There were statistically significant improvements compared to baseline in clinical scores and finger range of motion at the end of Phase 1 (

Fma: $\Delta \bar {x} = +3.69$ , $\sigma =3.54$ , p =0.0027;

Arat: $\Delta {\bar {\text {x}}} = +1.85$ , $\sigma =2.79$ , p =0.0346), and substantial but not significant improvements at the end of Phase 2 (

Fma: $\Delta {\bar {x}} = +2.67$ , $\sigma =4.15$ , p =0.0903;

Arat: $\Delta {\bar {\text {x}}} = +2.67$ , $\sigma =4.15$ , p =0.0903). Qualitative feedback indicated high user satisfaction, but highlighted areas for improvement. Usage declined during Phase 2, suggesting the need for engagement strategies. Despite these challenges, the study highlights HandMATE's potential to enhance recovery outcomes by addressing barriers to traditional rehabilitation settings. Future iterations will focus on increasing durability, portability, and user engagement, paving the way for broader adoption of home-based robotic rehabilitation devices.

Stroke is a leading cause of disability worldwide. HandMATE (Hand Movement Assisting Therapy Exoskeleton) addresses accessibility and cost related challenges associated with clinic-based stroke rehabilitation by providing a home-based robotic solution for hand rehabilitation. This study involved 14 chronic stroke patients (10 female, 4 male, average Fugl-Meyer Assessment (FMA) score of 24). Each took home and used a customized HandMATE over a 4-month period. During the first month (Phase 1), subjects had weekly clinic visits for troubleshooting problems and device improvements. During the next 3 months (Phase 2), subjects continued to use the device without weekly clinic visits. Assessments included the FMA, Action Research Arm Test (ARAT), motion capture, and device usage data. There were statistically significant improvements compared to baseline in clinical scores and finger range of motion at the end of Phase 1 (

Fma: Δx̄ = +3.69, σ = 3.54, p = 0.0027;

Arat: Δx̄ = +1.85, σ = 2.79, p = 0.0346), and substantial but not significant improvements at the end of Phase 2 (

Fma: Δx̄ = +2.67, σ = 4.15, p = 0.0903;

Arat: Δx̄ = +2.67, σ = 4.15, p = 0.0903). Qualitative feedback indicated high user satisfaction, but highlighted areas for improvement. Usage declined during Phase 2, suggesting the need for engagement strategies. Despite these challenges, the study highlights HandMATE's potential to enhance recovery outcomes by addressing barriers to traditional rehabilitation settings. Future iterations will focus on increasing durability, portability, and user engagement, paving the way for broader adoption of home-based robotic rehabilitation devices.

Stroke is a leading cause of long-term disability worldwide. With the advancements in sensor technologies and data availability, artificial intelligence (AI) holds the promise of improving the amount, quality and efficiency of care and enhancing the precision of stroke rehabilitation. We aimed to identify and characterize the existing research on AI applications in stroke recovery and rehabilitation of adults, including categories of application and progression of technologies over time. Data were collected from peer-reviewed articles across various electronic databases up to January 2024. Insights were extracted using AI-enhanced multi-method, data-driven techniques, including clustering of themes and topics. This scoping review summarizes outcomes from 704 studies. Four common themes (impairment, assisted intervention, prediction and imaging, and neuroscience) were identified, in which time-linked patterns emerged. The impairment theme revealed a focus on motor function, gait and mobility, while the assisted intervention theme included applications of robotic and brain-computer interface (BCI) techniques. AI applications progressed over time, starting from conceptualization and then expanding to a broader range of techniques in supervised learning, artificial neural networks (ANN), natural language processing (NLP) and more. Applications focused on upper limb rehabilitation were reviewed in more detail, with machine learning (ML), deep learning techniques and sensors such as inertial measurement units (IMU) used for upper limb and functional movement analysis. AI applications have potential to facilitate tailored therapeutic delivery, thereby contributing to the optimization of rehabilitation outcomes and promoting sustained recovery from rehabilitation to real-world settings.

Objective: To explore the potential use of artificial intelligence language models in formulating rehabilitation prescriptions and International Classification of Functioning, Disability and Health (ICF) codes.  Design: Comparative study based on a single case report compared to standard answers from a textbook.

Subjects: A stroke case from textbook.  Methods: Chat Generative Pre-Trained Transformer-4 (ChatGPT-4)was used to generate comprehensive medical and rehabilitation prescription information and ICF codes pertaining to the stroke case. This information was compared with standard answers from textbook, and 2 licensed Physical Medicine and Rehabilitation (PMR) clinicians reviewed the artificial intelligence recommendations for further discussion.

Results: ChatGPT-4 effectively formulated rehabilitation prescriptions and ICF codes for a typical stroke case, together with a rationale to support its recommendations. This information was generated in seconds. Compared with standard answers, the large language model generated broader and more general prescriptions in terms of medical problems and management plans, rehabilitation problems and management plans, as well as rehabilitation goals. It also demonstrated the ability to propose specified approaches for each rehabilitation therapy. The language model made an error regarding the ICF category for the stroke case, but no mistakes were identified in the ICF codes assigned.  Conclusion: This test case suggests that artificial intelligence language models have potential use in facilitating clinical practice and education in the field of rehabilitation medicine.

The integration of artificial intelligence (AI) technologies into physical and mental rehabilitation has the potential to significantly transform these fields. AI innovations, including machine learning algorithms, natural language processing, and computer vision, offer occupational therapists advanced tools to improve care quality. These technologies facilitate more precise assessments, the development of tailored intervention plans, more efficient treatment delivery, and enhanced outcome evaluation. This review explores the integration of AI across various aspects of rehabilitation, providing a thorough examination of recent advancements and current applications. It highlights how AI applications, such as natural language processing, computer vision, virtual reality, machine learning, and robotics, are shaping the future of physical and mental recovery in occupational therapy.

While stroke is one of the leading causes of disability, the prediction of upper limb (UL) functional recovery following rehabilitation is still unsatisfactory, hampered by the clinical complexity of post-stroke impairment. Predictive models leading to accurate estimates while revealing which features contribute most to the predictions are the key to unveil the mechanisms subserving the post-intervention recovery, prompting a new focus on individualized treatments and precision medicine in stroke. Machine learning (ML) and explainable artificial intelligence (XAI) are emerging as the enabling technology in different fields, being promising tools also in clinics. In this study, we had the twofold goal of evaluating whether ML can allow deriving accurate predictions of UL recovery in sub-acute patients, and disentangling the contribution of the variables shaping the outcomes. To do so, Random Forest equipped with four XAI methods was applied to interpret the results and assess the feature relevance and their consensus. Our results revealed increased performance when using ML compared to conventional statistical approaches. Moreover, the features deemed as the most relevant were concordant across the XAI methods, suggesting good stability of the results. In particular, the baseline motor impairment as measured by simple clinical scales had the largest impact, as expected. Our findings highlight the core role of ML not only for accurately predicting the individual outcome scores after rehabilitation, but also for making ML results interpretable when associated to XAI methods. This provides clinicians with robust predictions and reliable explanations that are key factors in therapeutic planning/monitoring of stroke patients.

Background: Physical disabilities become more common with advancing age. Rehabilitation restores function, maintaining independence for longer. However, the poor availability and accessibility of rehabilitation limits its clinical impact. Artificial Intelligence (AI) guided interventions have improved many domains of healthcare, but whether rehabilitation can benefit from AI remains unclear.

Methods: We conducted a systematic review of AI-supported physical rehabilitation technology tested in the clinical setting to understand: 1) availability of AI-supported physical rehabilitation technology; 2) its clinical effect; 3) and the barriers and facilitators to implementation. We searched in MEDLINE, EMBASE, CINAHL, Science Citation Index (Web of Science), CIRRIE (now NARIC), and OpenGrey.

Results: We identified 9054 articles and included 28 projects. AI solutions spanned five categories: App-based systems, robotic devices that replace function, robotic devices that restore function, gaming systems and wearables. We identified five randomised controlled trials (RCTs), which evaluated outcomes relating to physical function, activity, pain, and health-related quality of life. The clinical effects were inconsistent. Implementation barriers included technology literacy, reliability, and user fatigue. Enablers included greater access to rehabilitation programmes, remote monitoring of progress, reduction in manpower requirements and lower cost.

Conclusion: Application of AI in physical rehabilitation is a growing field, but clinical effects have yet to be studied rigorously. Developers must strive to conduct robust clinical evaluations in the real-world setting and appraise post implementation experiences.

Objective: The goal of this study was to compare the effect of different artificial intelligence (AI) machine learning and conventional therapy (CT) on upper limb impairments in patients with stroke.

Materials And Methods: PubMed, PubMed Central, Google Scholar, MEDLINE, Cochrane Library, Web of Science, Research Gate, and Wiley Online Library were searched. Descriptive statistics about variables were reported to calculate standardized mean differences in outcomes of motor control (the primary outcome), functional independence, upper extremity performance, and muscle tone. The Physiotherapy Evidence Database (PEDro) Scale was used to assess qualitative papers. The primary outcomes of AI and CT have been included in the meta-analyses.

Results: Ten papers with a total of 481 stroke patients were included and upper limb rehabilitation, upper limb functioning, and basic manual dexterity were examined. The heterogeneity test of the whole included measures (I2=45%) was medium. There were significant differences between the included measures (p-value=0.03) with a total SMD of 0.10 [0.01, 0.19]. According to the test for subgroup difference, it was found that there was a highly significant difference between the subgroups of the included measures (p-value=0.01) and the heterogeneity test (I2=59.8%).

Conclusions: AI is a feasible and safe method in post-stroke rehabilitation and improves upper-extremity function compared to CT. Significant AI post-treatment effects on upper-limb impairments have been observed. The findings showed that higher-quality evidence was detected in six assessment scales. However, a lower quality of evidence was detected in other scales. This indicated large or very large and consistent estimates of the treatment effects, and researchers were confident about the results. Therefore, the included observational studies are likely to provide an overestimate of the true effect.

Introduction: Pain assessment in individuals with cerebral palsy (CP), particularly those unable to self-report, is a significant challenge. Pain is the most common comorbidity in CP, yet current evaluation methods are often subjective and unreliable. An AI-based facial recognition system integrated into a mobile application could provide an objective, reliable tool for pain assessment in this population.

Objectives:

Methods: Three approaches were employed:

Results: A systematic review identified seven studies on automated facial recognition systems for pain detection. However, only one of these systems-ePAT/PainCheck-has been developed into a functional mobile application for clinical use, though not specific to individuals with cerebral palsy. This underscores the novelty of the current initiative. The feasibility of our proposed app was confirmed, and key technical and functional requirements were outlined, including intuitive design, dual local/cloud processing, and mechanisms for system improvement. Stakeholders emphasized ease of use, and suggested incorporating features such as accuracy estimation, offline functionality, multi-language support, and open communication fields.

Conclusions: This novel and feasible app represents a significant advance in pain assessment for CP, with potential applications in other neurological conditions with communication impairments and unique facial expressions.

Objective: Assessing pain in individuals with neurological conditions like cerebral palsy is challenging due to limited self-reporting and expression abilities. Current methods lack sensitivity and specificity, underlining the need for a reliable evaluation protocol. An automated facial recognition system could revolutionize pain assessment for such patients.The research focuses on two primary goals: developing a dataset of facial pain expressions for individuals with cerebral palsy and creating a deep learning-based automated system for pain assessment tailored to this group. Methods: The study trained ten neural networks using three pain image databases and a newly curated CP-PAIN Dataset of 109 images from cerebral palsy patients, classified by experts using the Facial Action Coding System. Results: The InceptionV3 model demonstrated promising results, achieving 62.67% accuracy and a 61.12% F1 score on the CP-PAIN dataset. Explainable AI techniques confirmed the consistency of crucial features for pain identification across models. Conclusion: The study underscores the potential of deep learning in developing reliable pain detection systems using facial recognition for individuals with communication impairments due to neurological conditions. A more extensive and diverse dataset could further enhance the models' sensitivity to subtle pain expressions in cerebral palsy patients and possibly extend to other complex neurological disorders. This research marks a significant step toward more empathetic and accurate pain management for vulnerable populations.

Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.

Background: Verbal communication is the "gold standard" for assessing pain. Consequently, individuals with communication disorders are particularly vulnerable to incomplete pain management. This review aims at identifying the current pain assessment instruments for adult patients with communication disorders.

Methods: A systematic review with meta-analysis was conducted on PubMed, PEDRO, EBSCOhost, VHL and Cochrane databases from 2011 to 2023 using MeSH terms "pain assessment, "nonverbal communication" and "communication disorders" in conjunction with additional inclusion criteria: studies limited to humans, interventions involving adult patients, and empirical investigations.

Results: Fifty articles were included in the review. Seven studies report sufficient data to perform the meta-analysis. Observational scales are the most common instruments to evaluate pain in individuals with communication disorders followed by physiological measures and facial recognition systems. While most pain assessments rely on observational scales, current evidence does not strongly endorse one scale over others for clinical practice. However, specific observational scales appear to be particularly suitable for identifying pain during certain potentially painful procedures, such as suctioning and mobilization, in these populations. Additionally, specific observational scales appear to be well-suited for certain conditions, such as mechanically ventilated patients.

Conclusions: While observational scales dominate pain assessment, no universal tool exists for adults with communication disorders. Specific scales exhibit promise for distinct populations, yet the diverse landscape of tools hampers a one-size-fits-all solution. Crucially, further high-quality research, offering quantitative data like reliability findings, is needed to identify optimal tools for various contexts. Clinicians should be informed to select tools judiciously, recognizing the nuanced appropriateness of each in diverse clinical situations.

Trial Registration: This systematic review is registered in PROSPERO (International prospective register of systematic reviews) with the

Id: CRD42022323655 .

Scoliosis is one of the most common spinal deformities, which affects millions of people worldwide. Bracing and physiotherapy exercises represent the first-line, non-invasive approaches for managing scoliosis. In recent years, the use of wearable devices has spread as a novel approach to the treatment of scoliosis. However, their effectiveness in treatment planning and outcomes has not been thoroughly evaluated. This manuscript provides a scoping review of the classification and application of wearable devices and the role of artificial intelligence (AI) in interpreting the data collected by wearable devices and guiding the treatment. A systematic search was carried out on Scopus, Web of Science, PubMed, and EMBASE for studies published between January 2020 and February 2025. A total of 269 studies were screened, and 88 articles were reviewed in depth. Inclusion criteria encompassed articles focusing on wearable devices integrated into smart braces, rehabilitation systems for scoliosis management, AI and machine-learning (ML) applications in scoliosis treatment, virtual reality (VR), and telemedicine for scoliosis care. The literature shows that the use of wearable devices can enhance scoliosis treatment by improving the efficiency of braces and enabling remote monitoring in rehabilitation programs. However, more research is needed to evaluate user compliance, long-term effectiveness, and the need for personalized interventions. Future advancements in artificial intelligence, microsensor technology, and data analytics may enhance the efficacy of these devices, which can lead to more personalized and accessible scoliosis treatment.

Introduction: Pain assessment in individuals with cerebral palsy (CP), particularly those unable to self-report, is a significant challenge. Pain is the most common comorbidity in CP, yet current evaluation methods are often subjective and unreliable. An AI-based facial recognition system integrated into a mobile application could provide an objective, reliable tool for pain assessment in this population.

Objectives:

Methods: Three approaches were employed:

Results: A systematic review identified seven studies on automated facial recognition systems for pain detection. However, only one of these systems-ePAT/PainCheck-has been developed into a functional mobile application for clinical use, though not specific to individuals with cerebral palsy. This underscores the novelty of the current initiative. The feasibility of our proposed app was confirmed, and key technical and functional requirements were outlined, including intuitive design, dual local/cloud processing, and mechanisms for system improvement. Stakeholders emphasized ease of use, and suggested incorporating features such as accuracy estimation, offline functionality, multi-language support, and open communication fields.

Conclusions: This novel and feasible app represents a significant advance in pain assessment for CP, with potential applications in other neurological conditions with communication impairments and unique facial expressions.

Introduction: Pain assessment in individuals with cerebral palsy (CP), particularly those unable to self-report, is a significant challenge. Pain is the most common comorbidity in CP, yet current evaluation methods are often subjective and unreliable. An AI-based facial recognition system integrated into a mobile application could provide an objective, reliable tool for pain assessment in this population.

Objectives:

Methods: Three approaches were employed:

Results: A systematic review identified seven studies on automated facial recognition systems for pain detection. However, only one of these systems-ePAT/PainCheck-has been developed into a functional mobile application for clinical use, though not specific to individuals with cerebral palsy. This underscores the novelty of the current initiative. The feasibility of our proposed app was confirmed, and key technical and functional requirements were outlined, including intuitive design, dual local/cloud processing, and mechanisms for system improvement. Stakeholders emphasized ease of use, and suggested incorporating features such as accuracy estimation, offline functionality, multi-language support, and open communication fields.

Conclusions: This novel and feasible app represents a significant advance in pain assessment for CP, with potential applications in other neurological conditions with communication impairments and unique facial expressions.

Background: Diabetic foot ulcers (DFUs) affect 19% to 34% of individuals with diabetes during their lifetime and account for over one million nontraumatic lower-limb amputations annually. Standard care often fails to detect early, subclinical changes, leading to delayed intervention and high mortality rates. This review examines how artificial intelligence (AI) and machine learning (ML) can extract complex patterns from diverse data modalities to advance DFU care.

Methods: We examined AI/ML applications in DFU care across four domains: diagnosis (automated image and thermogram classification, biomechanical risk stratification), treatment optimization (AI-driven offloading prescriptions, tele-rehabilitation, molecularly informed wound care), prognosis (neural network and random forest models for risk assessment), and novel strategy development (generative AI, transcriptomic target discovery, wearable digital biomarkers).

Results: Artificial intelligence/ML methodologies have demonstrated promising results in DFU image and thermogram analysis, with reported accuracies ranging from 81-97% across different studies. Biomechanical ML models show potential for dynamic risk stratification, and prognostic models achieve moderate performance with area under the curve values around 0.74-0.82. Generative AI approaches have shown promise for data augmentation, improving segmentation performance in limited datasets.

Conclusion: Despite promising advances, several challenges impede clinical translation, including data standardization, model explainability, regulatory compliance, clinical workflow integration, prospective validation, and equitable implementation. Collaborative efforts among clinicians, data scientists, regulators, and patients are essential to translate AI-driven innovations into routine DFU management, potentially reducing amputations and improving outcomes for this global health burden.

Objective: To develop and validate machine learning-based models for predicting independent walking ability in children with cerebral palsy (CP).

Design: Retrospective cohort study.

Setting: Data were collected from a national CP registry platform and follow-up assessments were conducted through telephone interviews.

Participants: Children with CP (n=807) registered between January 2016 and December 2020, with follow-up data collected from October 2022 to March 2023.

Interventions: Not applicable.

Main Outcome Measures: The primary outcome was independently walking before the age of 6 years.

Results: Among the 807 participants, 561 (69.5%) achieved independent walking. Univariate Cox regression identified several predictive factors, including neonatal asphyxia, bilirubin encephalopathy, Gross Motor Function Classification System level before age of 2 years, age of independent sitting, type of CP, magnetic resonance imaging classification, Gross Motor Function Measure-88 scores, epilepsy, intellectual disability, early preterm birth, and very low birth weight (P<.05). Machine learning models demonstrated excellent predictive performance, with logistic regression achieving the highest area under the curve (AUC=0.947), followed by XGBoost (AUC=0.946) and multilayer perceptron (AUC=0.945). Cox proportional hazard models identified key predictors for the timing of independent walking, with a nomogram constructed for clinical application. Internal validation confirmed model reliability, although calibration curves indicated potential overestimation for ages 5-6 years.

Conclusions: Machine learning models accurately predict independent walking ability in children with CP, although calibration analyses indicated potential overestimation for children aged 5-6 years. The proposed nomogram provides clinicians with an interpretable tool for personalized prognosis. Although internal validation demonstrated excellent performance, future external validation in multicenter cohorts will be critical to confirm generalizability.

Objective: To develop and validate machine learning-based models for predicting independent walking ability in children with cerebral palsy (CP).

Design: Retrospective cohort study.

Setting: Data were collected from a national CP registry platform and follow-up assessments were conducted through telephone interviews.

Participants: Children with CP (n=807) registered between January 2016 and December 2020, with follow-up data collected from October 2022 to March 2023.

Interventions: Not applicable.

Main Outcome Measures: The primary outcome was independently walking before the age of 6 years.

Results: Among the 807 participants, 561 (69.5%) achieved independent walking. Univariate Cox regression identified several predictive factors, including neonatal asphyxia, bilirubin encephalopathy, Gross Motor Function Classification System level before age of 2 years, age of independent sitting, type of CP, magnetic resonance imaging classification, Gross Motor Function Measure-88 scores, epilepsy, intellectual disability, early preterm birth, and very low birth weight (P<.05). Machine learning models demonstrated excellent predictive performance, with logistic regression achieving the highest area under the curve (AUC=0.947), followed by XGBoost (AUC=0.946) and multilayer perceptron (AUC=0.945). Cox proportional hazard models identified key predictors for the timing of independent walking, with a nomogram constructed for clinical application. Internal validation confirmed model reliability, although calibration curves indicated potential overestimation for ages 5-6 years.

Conclusions: Machine learning models accurately predict independent walking ability in children with CP, although calibration analyses indicated potential overestimation for children aged 5-6 years. The proposed nomogram provides clinicians with an interpretable tool for personalized prognosis. Although internal validation demonstrated excellent performance, future external validation in multicenter cohorts will be critical to confirm generalizability.

Objective: To develop and validate machine learning-based models for predicting independent walking ability in children with cerebral palsy (CP).

Design: Retrospective cohort study.

Setting: Data were collected from a national CP registry platform and follow-up assessments were conducted through telephone interviews.

Participants: Children with CP (n=807) registered between January 2016 and December 2020, with follow-up data collected from October 2022 to March 2023.

Interventions: Not applicable.

Main Outcome Measures: The primary outcome was independently walking before the age of 6 years.

Results: Among the 807 participants, 561 (69.5%) achieved independent walking. Univariate Cox regression identified several predictive factors, including neonatal asphyxia, bilirubin encephalopathy, Gross Motor Function Classification System level before age of 2 years, age of independent sitting, type of CP, magnetic resonance imaging classification, Gross Motor Function Measure-88 scores, epilepsy, intellectual disability, early preterm birth, and very low birth weight (P<.05). Machine learning models demonstrated excellent predictive performance, with logistic regression achieving the highest area under the curve (AUC=0.947), followed by XGBoost (AUC=0.946) and multilayer perceptron (AUC=0.945). Cox proportional hazard models identified key predictors for the timing of independent walking, with a nomogram constructed for clinical application. Internal validation confirmed model reliability, although calibration curves indicated potential overestimation for ages 5-6 years.

Conclusions: Machine learning models accurately predict independent walking ability in children with CP, although calibration analyses indicated potential overestimation for children aged 5-6 years. The proposed nomogram provides clinicians with an interpretable tool for personalized prognosis. Although internal validation demonstrated excellent performance, future external validation in multicenter cohorts will be critical to confirm generalizability.

Aim: Our aim was to investigate perinatal and clinical factors associated with children with cerebral palsy (CP) using magnetic resonance imaging (MRI). The distribution of MRI patterns was based on the MRI classification system (MRICS). Associations between perinatal/clinical characteristics and MRI patterns were also investigated.

Methods: A population-based cohort study was performed; those 257 children (58.0% male) were enrolled from our CP database who born between 1990 and 2015 in Southwest Hungary and had at least one MRI scan.

Results: Brain maldevelopments were found in 18.7% of our patients, 83.7% of those born at term. Grey matter lesions were found in 19.8% of our patients, and 80.0% of those children were born at term. The rate of white matter injuries was the highest (35.4%); 69.0% of these patients were born before 37th week of gestation. MRI revealed no abnormalities in 13.6% of children with CP. The best values of gross/fine motor and cognitive function tests were found in children with normal MRI and with grey matter injuries. The prevalence of epilepsy was above 60% in every group with an abnormal MRI.

Conclusion: MRI results were conclusive in 86.4% of children with CP. It is highly encouraged to perform cranial MRI in every patient with CP.

Aim: The aim of this study was to describe the distribution of magnetic resonance imaging (MRI) patterns in a large population sample of children with cerebral palsy (CP) and to examine associations between MRI patterns, and antenatal and perinatal variables.

Method: Data were retrieved from the Victorian CP Register for 884 children (527 males, 357 females) born between 1999 and 2006. Postneonatal MRI was classified for 594 children. For 563 children (329 males, 234 females) for whom classification was to a single MRI pattern, the frequency of each variable was compared between patterns and with the population frequency.

Results: White matter injury was the most common MRI pattern (45%), followed by grey matter injury (14%), normal imaging (13%), malformations (10%), focal vascular insults (9%), and miscellaneous patterns (7%). Parity, birth gestation, level of neonatal care, Apgar score, and time to established respiration varied between MRI patterns (p<0.01). Nulliparity was most strongly associated with focal vascular insults, whereas multiparity was associated only with malformations. Grey matter injury was not associated with birth in a tertiary unit, but was strongly associated with severe perinatal compromise. The frequency of neonatal seizures and of nursery admissions was lowest among children with malformations.

Interpretation: As known risk factors for CP are differentially associated with specific MRI patterns, future exploration of causal pathways might be facilitated when performed in pathogenically defined groups.

Background: The Gross Motor Function Measure (GMFM-88) is commonly used in the evaluation of gross motor function in children with cerebral palsy (CP). The relative reliability of GMFM-88 has been assessed in children with CP. However, little information is available regarding the absolute reliability or responsiveness of GMFM-88.

Objective: The purpose of this study was to determine the absolute and relative reliability and the responsiveness of the GMFM-88 in evaluating gross motor function in children with CP.

Design: A clinical measurement design was used.

Methods: Ten raters scored the GMFM-88 in 84 children (mean age=3.7 years, SD=1.9, range=10 months to 9 years 9 months) from video records across all Gross Motor Function Classification System (GMFCS) levels to establish interrater reliability. Two raters participated to assess intrarater reliability. Responsiveness was determined from 3 additional assessments after the baseline assessment. The interrater and intrarater intraclass correlation coefficients (ICCs) with 95% confidence intervals, standard error of measurement (SEM), smallest real difference (SRD), effect size (ES), and standardized response mean (SRM) were calculated.

Results: The relative reliability of the GMFM was excellent (ICCs=.952-1.000). The SEM and SRD for total score of the GMFM were acceptable (1.60 and 3.14, respectively). Additionally, the ES and SRM of the dimension goal scores increased gradually in the 3 follow-up assessments (GMFCS levels I and

Ii: ES=0.5, 0.6, and 0.8 and SRM=1.3, 1.8, and 2.0; GMFCS levels III-

V: ES=0.4, 0.7, and 0.9 and SRM=1.5, 1.7, and 2.0).

Limitations: Children over 10 years of age with CP were not included in this study, so the results should not be generalized to all children with CP.

Conclusions: Both the reliability and the responsiveness of the GMFM-88 are reasonable for measuring gross motor function in children with CP.

Severity of cerebral palsy was based solely on GMFCS level, which is
a reliable and valid system that classifies children with cerebral palsy by
their age-specific gross motor activity.
The GMFCS describes the major functional characteristics of children with
cerebral palsy in each level within the following age windows: prior to second
birthday; between age 2 years and fourth birthday; between age 4 years and
sixth birthday; and between ages 6 and 12 years.
The outlines the main
abilities of children aged 6 to 12 years in each GMFCS level. Use of the GMFCS
requires familiarity with the child, but is not a test and requires no formal
training.

Motor function was assessed with the GMFM.
The GMFM is a widely used, criterion-referenced, clinical observation tool
with a scale from 0-100 that was developed and validated for children with
cerebral palsy or Down syndrome. It has excellent
reliability and demonstrated ability to evaluate meaningful change in gross
motor function in children diagnosed as having cerebral palsy.
The GMFM was not designed to compare the function of children with cerebral
palsy to typically developing children. It measures gross motor function in
lying and rolling, crawling and kneeling, sitting, standing, and walk-run-jump
activities. It can be used with any child or adolescent diagnosed as having
cerebral palsy. It focuses on the extent of achievement of a variety of gross
motor activities (mainly mobility skills and activities requiring postural
control such as sitting, kneeling, and standing on 1 foot) that a typically
developing 5-year-old could accomplish. For data analyses, we used scores
derived from the GMFM-66, a measure with interval levels that was developed
by Rasch analysis of the original 88-item scale (GMFM-88).

Aim: The purpose of this study was to review published research on the use of the Gross Motor Function Measure (GMFM-88) and (GMFM-66) as outcome measures to determine if these tools detect changes in gross motor function in children with cerebral palsy (CP) undergoing interventions.

Methods: A comprehensive literature search was conducted using Medline and PubMed to identify studies published from January 2000 through January 2011 that reported the accuracy of GMFM-88 and GMFM-66 to measure changes over time in children with CP undergoing interventions. The keywords used for the search were "GMFM" and "CP". Two of the authors (M.A. and S.B.) reviewed the titles and abstracts found in the databases. The methodological quality of the studies was assessed by using the Critical Review Form-Quantitative Studies.

Results: Of 62 papers initially identified, 21 studies fulfilled the inclusion criteria. These articles consist of three longitudinal studies, six randomized controlled trials, four repeated measure design, six pre-post test design, a case series and one non-randomized prospective study. The included studies were generally of moderate to high methodological quality. The studies included children from a wide age range of 10 months to 16 years. According to the National Health and Medical Research Council, the study designs were level II, III-2, III-3 and IV.

Conclusion: The review suggests that the GMFM-88 and GMFM-66 are useful as outcome measures to detect changes in gross motor function in children with CP undergoing interventions. Implications for Rehabilitation Accurate measurement of change in gross motor skill acquisition is important to determine effectiveness of intervention programs in children with cerebral palsy (CP). The Gross Motor Function Measure (GMFM-88 and GMFM-66) are common tools used by rehabilitation specialists to measure gross motor function in children with CP. The GMFM appears to be an effective outcome tool for measuring change in gross motor function according to a small number of randomized control studies utilizing participant populations of convenience.

Objective: To develop and validate machine learning-based models for predicting independent walking ability in children with cerebral palsy (CP).

Design: Retrospective cohort study.

Setting: Data were collected from a national CP registry platform and follow-up assessments were conducted through telephone interviews.

Participants: Children with CP (n=807) registered between January 2016 and December 2020, with follow-up data collected from October 2022 to March 2023.

Interventions: Not applicable.

Main Outcome Measures: The primary outcome was independently walking before the age of 6 years.

Results: Among the 807 participants, 561 (69.5%) achieved independent walking. Univariate Cox regression identified several predictive factors, including neonatal asphyxia, bilirubin encephalopathy, Gross Motor Function Classification System level before age of 2 years, age of independent sitting, type of CP, magnetic resonance imaging classification, Gross Motor Function Measure-88 scores, epilepsy, intellectual disability, early preterm birth, and very low birth weight (P<.05). Machine learning models demonstrated excellent predictive performance, with logistic regression achieving the highest area under the curve (AUC=0.947), followed by XGBoost (AUC=0.946) and multilayer perceptron (AUC=0.945). Cox proportional hazard models identified key predictors for the timing of independent walking, with a nomogram constructed for clinical application. Internal validation confirmed model reliability, although calibration curves indicated potential overestimation for ages 5-6 years.

Conclusions: Machine learning models accurately predict independent walking ability in children with CP, although calibration analyses indicated potential overestimation for children aged 5-6 years. The proposed nomogram provides clinicians with an interpretable tool for personalized prognosis. Although internal validation demonstrated excellent performance, future external validation in multicenter cohorts will be critical to confirm generalizability.

Artificial intelligence (AI) is transforming personalized rehabilitation by introducing innovative methods to enhance care across diverse medical specialties. Despite its potential, widespread implementation remains limited, largely due to a lack of comprehensive analyses on its benefits and barriers. This mini narrative review examines current applications of AI in personalized rehabilitation and provide a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis AI is already being used to develop personalized treatment plans, support ongoing patient management, and adapt therapy sessions in real-time. One of its key strengths is the capacity to process vast datasets and monitor real-time information, thereby elevating the level of personalization. Automation of certain tasks can reduce human error and alleviate clinician workload, allowing more time for direct patient care. Opportunities for AI lie in leveraging rapidly advancing technologies to meet the rising demand for rehabilitation services, particularly with aging populations. Collaborations with industry can accelerate innovation, while data sharing can promote best practices across institutions. However, notable challenges persist. High implementation costs, ethical concerns such as algorithmic bias, and risks of increasing healthcare disparities remain major barriers. Additionally, threats such as data privacy breaches and security vulnerabilities emphasize the need for robust, balanced regulatory frameworks. In conclusion, AI holds immense promise for transforming personalized rehabilitation. While current applications are largely in early stages or proof-of-concept phases, ongoing research, ethical foresight, and strategic collaboration are essential to maximize benefits and minimize risks for optimal patient outcomes.

Successful publishing of an article depends on several factors, including the structure of the main text, the so-called introduction, methods, results and discussion structure (IMRAD). The first objective of our work is to provide recent results on the number of paragraphs (pars.) per section used in articles published in major medical journals. Our second objective is the investigation of other structural elements, i.e., number of tables, figures and references and the availability of supplementary material. We analyzed data from randomly selected original articles published in years 2005, 2010 and 2015 from the journals The BMJ, The Journal of the American Medical Association, The Lancet, The New England Journal of Medicine and PLOS Medicine. Per journal and year 30 articles were investigated. Random effect meta-analyses were performed to provide pooled estimates. The effect of time was analyzed by linear mixed models. All articles followed the IMRAD structure. The number of pars. per section increased for all journals over time with 1.08 (95% confidence interval (CI): 0.70-1.46) pars. per every two years. The largest increase was observed for the methods section (0.29 pars. per year; 95% confidence interval (CI): 0.19-0.39). PLOS Medicine had the highest number of pars. The number of tables did not change, but number of figures and references increased slightly. Not only the standard IMRAD structure should be used to increase the likelihood for publication of an article but also the general layout of the target journal. Supplementary material has become standard. If no journal-specific information is available, authors should use 3/10/9/8 pars. for the introduction/methods/results/discussion sections.

Writing and publishing scientific papers have become requisites for all scientists (researchers and academics alike) to maintain their professional career. The prospects of writing a scientific paper are often regarded as somewhat daunting to the uninitiated. However a universal, well established structure format known as "IMRAD": i.e. Introduction, Methods, Results And Discussion has been established. This paper details this methodology.

Biomedical texts can be typically represented by four rhetorical categories: Introduction, Methods, Results and Discussion (IMRAD). Classifying sentences into these categories can benefit many other text-mining tasks. Although many studies have applied different approaches for automatically classifying sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles. We first evaluated whether sentences in full-text biomedical articles could be reliably annotated into the IMRAD format and then explored different approaches for automatically classifying these sentences into the IMRAD categories. Our results show an overall annotation agreement of 82.14% with a Kappa score of 0.756. The best classification system is a multinomial naïve Bayes classifier trained on manually annotated data that achieved 91.95% accuracy and an average F-score of 91.55%, which is significantly higher than baseline systems. A web version of this system is available online at-http://wood.ims.uwm.edu/full_text_classifier/.

Scoliosis is one of the most common spinal deformities, which affects millions of people worldwide. Bracing and physiotherapy exercises represent the first-line, non-invasive approaches for managing scoliosis. In recent years, the use of wearable devices has spread as a novel approach to the treatment of scoliosis. However, their effectiveness in treatment planning and outcomes has not been thoroughly evaluated. This manuscript provides a scoping review of the classification and application of wearable devices and the role of artificial intelligence (AI) in interpreting the data collected by wearable devices and guiding the treatment. A systematic search was carried out on Scopus, Web of Science, PubMed, and EMBASE for studies published between January 2020 and February 2025. A total of 269 studies were screened, and 88 articles were reviewed in depth. Inclusion criteria encompassed articles focusing on wearable devices integrated into smart braces, rehabilitation systems for scoliosis management, AI and machine-learning (ML) applications in scoliosis treatment, virtual reality (VR), and telemedicine for scoliosis care. The literature shows that the use of wearable devices can enhance scoliosis treatment by improving the efficiency of braces and enabling remote monitoring in rehabilitation programs. However, more research is needed to evaluate user compliance, long-term effectiveness, and the need for personalized interventions. Future advancements in artificial intelligence, microsensor technology, and data analytics may enhance the efficacy of these devices, which can lead to more personalized and accessible scoliosis treatment.