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An introductory course to a topic that’s rapidly transforming the landscape of rehabilitation—Artificial Intelligence in occupational and physical therapy. Whether you’re completely new to AI or just beginning to explore its potential, this session is designed to meet you at an introductory level and build your confidence.

Together, we’ll define core AI concepts and break down key terms like machine learning, natural language processing, and computer vision—all in the context of OT and PT. You’ll discover real AI-powered tools that are already being used to support therapists, along with practical applications to enhance patient engagement, remote monitoring, and individualized care.

We’ll also touch on important ethical considerations, such as privacy and data bias, and discuss the impact of AI on our own cognitive skills as clinicians.

Most importantly, you’ll walk away with clinical takeaways you can begin using immediately—ideas and tools you can integrate into your practice right after this course.

Dr Aditi the OT - CEU Course Title Screen and Description

Learning Objectives

  1. Define artificial intelligence (AI) and distinguish between key concepts such as machine learning, natural language processing, and computer vision within the context of rehabilitation.
  2. Identify at least three AI-powered tools or platforms currently available to support occupational and physical therapy practice.
  3. List at least three practical applications of AI in occupational and physical therapy settings.
  4.  Identify ways AI can enhance patient engagement, enable remote monitoring, and support the development of individualized home programs in OT and PT.
  5. List ethical considerations related to the use of AI in clinical practice, including privacy concerns and data bias.
  6. Identify the potential impact of relying on AI tools on clinicians’ and clients’ cognitive skills.

 

To enroll in the course and take the quiz, enter your information here: You can enroll in this course from your student dashboard. You need to be logged in.

Course Lessons :

 

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References

1. Hulsen, T., & Scheper, M. (2025). AI in Healthcare: Do Not Forget About Allied Healthcare. AI.

2. Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317–1318. https://doi.org/10.1001/jama.2017.18391

3. Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94

4. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., … & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25, 24–29. https://doi.org/10.1038/s41591-018-0316-z

5. Gadkari, S. (2023). Using ChatGPT in Occupational Therapy. OT Potential. https://www.otpotential.com/blog/chatgpt-ot

6. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., … & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101

7. Jotterand, F., & Bosco, C. (2020). Keeping the human in the loop: Ethical considerations in AI use in healthcare. AJOB Neuroscience, 11(3), 175–185.

8. Kim, H., Park, J., & Lee, H. (2020). Machine learning-based robotic glove system for stroke rehabilitation. Journal of NeuroEngineering and Rehabilitation, 17(1), 56.

9. Krittanawong, C., Johnson, K. W., Rosenson, R. S., Wang, Z., Aydar, M., & Kitai, T. (2021). Deep learning for cardiovascular medicine: A practical primer. European Heart Journal, 42(21), 2077–2089. https://doi.org/10.1093/eurheartj/ehaa930

10. OT Potential. (2023). AI in Documentation Tools for Therapists. https://otpotential.com/blog/ai-documentation-tools

11. Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., … & Salimans, T. (2022). Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2204.06125. https://doi.org/10.48550/arXiv.2204.06125

12. Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

13. Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M. A., Lacroix, T., … & Scialom, T. (2023). LLaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971. https://doi.org/10.48550/arXiv.2302.13971

14. Moons, P., & Van Bulck, L. (2023). ChatGPT: can artificial intelligence language models be of value for cardiovascular nurses and allied health professionals. European journal of cardiovascular nursing, 22(7), e55-e59.

15. Escobar, S. J., Hans, A., Lefebvre, M., & Yu, K. (2023). The Use of Artificial Intelligence in Pediatric Occupational Therapy Interventions: Parent and Caregiver Perspectives. Stanbridge University.

16. Kaelin, V. C., Nilsson, I., & Lindgren, H. (2024). Occupational therapy in the space of artificial intelligence: Ethical considerations and human-centered efforts. Scandinavian Journal of Occupational Therapy, 31(1). https://doi.org/10.1080/11038128.2024.2421355

17. Mansour, T., & Wong, J. (2024). Enhancing fieldwork readiness in occupational therapy students with generative Al. Frontiers in Medicine, 11, 1485325. https://doi.org/10.3389/fmed.2024.1485325

18. Rangasrinivasan, S., Suresh, M. S. S., Olszewski, A., Setlur, S., Jayaraman, B., & Govindaraju, V. (2025). AI-enhanced child handwriting analysis: A framework for the early screening of dyslexia and dysgraphia. SN Computer Science, 6, Article 399. https://doi.org/10.1007/s42979-025-03927-0

19. Denche-Zamorano, A., Rodriguez-Redondo, Y., Barrios-Fernandez, S., Mendoza-Muñoz, M., Castillo-Paredes, A., Rojo-Ramos, J., Garcia-Gordillo, M. A., & Adsuar, J. C. (2023). Rehabilitation Is the Main Topic in Virtual and Augmented Reality and Physical Activity Research: A Bibliometric Analysis. Sensors (Basel, Switzerland), 23(6), 2987. https://doi.org/10.3390/s23062987

20. Rasa A. R. (2024). Artificial Intelligence and Its Revolutionary Role in Physical and Mental Rehabilitation: A Review of Recent Advancements. BioMed research international, 2024, 9554590. https://doi.org/10.1155/bmri/9554590

21. Pergantis, P., Christopoulos, G. I., Gkougkousis, I., Poulakou-Rebelakou, E., & Peppé, S. (2024). Assistive and emerging technologies to detect and reduce neurophysiological stress and anxiety in children and adolescents with autism and sensory processing disorders: A systematic review. MDPI Electronics, 13(3), 590. https://doi.org/10.3390/electronics13030590