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Artificial Intelligence (AI) In Health Care and Rehabilitation

Original Editor - Lucinda hampton

Top Contributors - Lucinda hampton, Ines Musabyemariya, Angeliki Chorti and Alexandra Stead  

This article is currently under review and may not be up to date. Please come back soon to see the finished work! (2/03/2026)


Introduction

Artificial Intelligence (AI) can be viewed as the fourth industrial revolution and the emerging frontier in medicine.[1] AI refers to the capability of a machine to perform a functional task overseen insightfully by humans. AI applies algorithms to learn, think, and then ultimately aid various clinical practices such as radiology and rehabilitation. AI is also used to find all relevant current information from journals, books, and evidence-based practice, helping in clinical decision-making in healthcare. Moreover, AI technologies aids in the reduction of medical errors in healthcare practices.[2]

The use of AI technologies is swiftly growing in healthcare and rehabilitation. As health professionals, we need to increase our awareness AI's applications in rehabilitation to provide best practice patient care.[2]

Applications of AI in Healthcare

AI in the orthopaedic surgery and sports medicine has been applied in various areas such as predicting athlete injury risk, interpretation of medical imaging, evaluating patient-reported outcomes etc. AI in rehabilitation can provide a thorough assessment, forecasting patients’ performance and has the potential to establish a diagnosis. AI in medical and rehabilitation practices can also be applied to problem solving, x-ray diagnosis and protocols for best practice. However, as with all new knowledge, this emerging technology will require a fundamental working comprehension of the strengths, limitations, and applications of AI-based tools.[2] You can find more information by reading Artificial Intelligence (AI) in Sports.

Pro-Robotic-Gait-Training.

AI also has potential in neurologic physiotherapy. Computer-aided diagnosis (CAD) systems using AI and modern signal processing methods have the ability to help clinicians in analysing and interpreting physiological signals and images more effectively in Neurological Disorders such as epilepsy, Parkinson's disease, Alzheimer's disease, multiple sclerosis, and ischemic brain stroke.[3] Recent research and relevant data accumulation are actively developing an increasingly number of effective algorithms, aiding in our understanding of complex brain mechanism.[4]

Development in AI technology has been a common focus, involving robotic-assisted therapy, motor function and gait assessment, assessment of level of function, upper extremity recovery, and movement. AI sensors can recognise abnormal movement patterns during functional movements and can be of great value in analysis of functional tasks, and prescription of personalised treatment plans.[5]

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AI Challenges

In the future as a profession, we must learn to embrace AI by knowing how to best use it to optimise our practice. It is important that professionals understand how to analyse and interpret AI generated algorithms, apply clinical judgement to it and integrate it into practice in ethical, professional and social contexts. Ultimately, the goal as a profession is to utilise AI to our advantage to improve our clinical outcomes and be open to seeing use of AI as a collaborative tool rather then a threat.[6] Challenges at present are evolving, but AI literacy is a common factor that can impact AI's use and integration.[7]

Situation in Low- and Middle-Income Countries

AI is rapidly emerging as a transformative technology in the healthcare industry, particularly in low- and middle-income countries (LMICs). With its potential to address the persistent challenges of inadequate human resources, limited access to quality healthcare systems, and communicable diseases, AI is being leveraged for diverse applications including diagnosis, disease control, patient feedback, and health system strengthening. The integration of AI in LMICs is expected to have a significant impact on healthcare delivery and health outcomes, thereby contributing to the global agenda of achieving universal health coverage. [8][9]

In addition to disease diagnosis and outbreak prediction, AI also shows great rehabilitation potential in low- and middle-income countries (LMICs). AI-powered rehabilitation tools are being developed to help patients recover from injuries, illnesses, and disabilities. These tools are especially helpful in areas with a shortage of healthcare professionals and limited access to rehabilitation facilities. [10] AI-powered rehabilitation tools include virtual reality systems, wearable devices, and mobile apps that help patients perform exercises and track their progress. This can improve patient outcomes, reduce healthcare costs, and increase access to quality rehabilitation services in LMICs.

Resources

Ethics and governance of artificial intelligence for health: guidance on large multi-modal models.

References

  1. ↑ Ramkumar PN, Luu BC, Haeberle HS, Karnuta JM, Nwachukwu BU, Williams RJ. Sports medicine and artificial intelligence: a primer. Am J Sports Med. 2022 Mar;50(4):1166-74.
  2. ↑ 2.0 2.1 2.2 Alsobhi M, Khan F, Chevidikunnan MF, Basuodan R, Shawli L, Neamatallah Z. Physical Therapists’ Knowledge and Attitudes Regarding Artificial Intelligence Applications in Health Care and Rehabilitation: Cross-sectional Study. J Med Internet Res. 2022 Oct 20;24(10):e39565.
  3. ↑ Raghavendra U, Acharya UR, Adeli H. Artificial intelligence techniques for automated diagnosis of neurological disorders. Eur Neur. 2020 Nov 19;82(1-3):41-64.
  4. ↑ Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng. 2020 Dec 1;4(4):041503.
  5. ↑ Fulk G. Artificial Intelligence and Neurologic Physical Therapy. J Neurol Phys Ther. 2023 Jan 1;47(1):1-2.
  6. ↑ Rowe M, Nicholls DA, Shaw J. How to replace a physiotherapist: artificial intelligence and the redistribution of expertise. Physiother Theory Pract. 2022 Nov 18;38(13):2275-83.
  7. ↑ Khalil Kimiafar, Masoumeh Sarbaz, Seyyed Mohammad Tabatabaei, Kosar Ghaddaripouri, Mousavi A, Marziyeh Raei Mehneh, et al. Artificial Intelligence Literacy Among Healthcare Professionals and Students: A Systematic Review. Frontiers in health informatics. 2023 Nov 11;12:168–8.
  8. ↑ Alami H, Rivard L, Lehoux P, Hoffman SJ, Cadeddu SBM, Savoldelli M, Abdoulaye Samri M, Ali Ag Ahmed M, Fleet R, Fortin J-P. Artificial intelligence in health care: laying the Foundation for Responsible, sustainable, and inclusive innovation in low- and middle-income countries. Globalization and Health. 2020 Jun 24;16(1):52.
  9. ↑ Sumner J, Lim HW, Chong LS, Bundele A, Mukhopadhyay A, Kayambu G. Artificial intelligence in physical rehabilitation: A systematic review. Artificial Intelligence in Medicine. 2023 Dec 1;146:102693.
  10. ↑ Bright T, Wallace S, Kuper H. A Systematic Review of Access to Rehabilitation for People with Disabilities in Low- and Middle-Income Countries. Int J Environ Res Public Health. 2018 Oct 2;15(10):2165
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