Artificial Intelligence and Digital Divide in Physiotherapy Education
Open accessAbstract
The potential of artificial intelligence (AI) in health care and education has become increasingly evident, promising to revolutionize how healthcare professionals deliver services and how learners engage with educational content. AI enhances individualized student learning experiences and transforms education delivery by adapting to emerging healthcare advancements. We emphasize the current need for more exploration of AI's applications in day-to-day education in physiotherapy schools. We conducted a PubMed search, revealing a significant gap in research on AI in physiotherapy education compared to medical and dental education. Knowledge gaps and varied perspectives among Canadian healthcare students, including physiotherapy students, highlight the need for targeted educational strategies and ethical considerations. We conclude with a call to bridge the digital divide in physiotherapy education, stressing the importance of integrating AI to empower students and foster innovation in physiotherapy education.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Knowledge in integrating GAI into health professional education is limited.
“Knowledge in integrating GAI into health professional education is limited.”
From Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study (2025) · cites this paper as [17]
The Editorial (page unavailable) describes limited day-to-day exploration of AI in education and a paucity of research specifically in physiotherapy education. It also reports that some Canadian healthcare students felt inadequately informed about AI. The paper does not establish that knowledge about integrating generative AI (GAI) is limited across health professional education generally. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThere is a significant gap in AI applications in physiotherapy education compared to medical and dental education.
“However, there is a significant gap in AI applications in physiotherapy education compared to medical and dental education [17].”
From Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study (2025) · cites this paper as [17]
The Editorial reports a substantial gap in research on AI in physiotherapy education compared with medical and dental education, and says physiotherapy is probably lagging behind. It does not establish a significant comparative gap in the actual use of AI applications in education; that is stronger than its literature-search finding. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTailored GAI applications can analyze patient data, recommend personalized treatment plans, and conduct clinical simulations.
“Tailored GAI applications can analyze patient data, recommend personalized treatment plans, and conduct clinical simulations [17].”
From Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study (2025) · cites this paper as [17]
The Editorial (page not provided) says specific AI applications in physiotherapy education could analyze patient data, recommend personalized treatment plans, and enhance clinical simulations. It does not identify those applications as generative AI (GAI) or say they conduct simulations. (LLM, conf=0.84)
Experimental — accuracy not yet measuredRecent research has shown that GAI holds significant potential for enhancing physiotherapy practice.
“Recent research has shown that GAI holds significant potential for enhancing physiotherapy practice.”
From Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study (2025) · cites this paper as [17]
The Editorial (page unavailable) says AI has the potential to play an increasingly important role in physiotherapy assessment and interventions. But it discusses AI broadly, not generative AI (GAI) specifically, and presents the benefit as a prospect rather than a demonstrated research finding about physiotherapy practice. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Health Care Students’ Perspectives on Artificial Intelligence: Countrywide Survey in Canada
Teng, M., Singla, R., Yau, O., Lamoureux, D., Gupta, A., Hu, Z., Hu, R., Aissiou, A., Eaton, S., Hamm, C., et al. (2022) · JMIR Medical Education · doi:10.2196/33390 · Cited by 214 · see citing works
Applications of Artificial Intelligence (AI) in Medical Education: A Scoping Review
Nagi, F., Salih, R., Alzubaidi, M., Shah, H., Alam, T., Shah, Z., & Househ, M. (2023) · Studies in Health Technology and Informatics · doi:10.3233/shti230581 · Cited by 135 · see citing works
Everard, G. J., Declerck, L., Detrembleur, C., LEONARD, S., BOWER, G., Dehem, S., & Lejeune, T. M. (2022) · European Journal of Physical and Rehabilitation Medicine · doi:10.23736/s1973-9087.22.07404-4 · Cited by 57 · see citing works
Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework
Azzi, S., Gagnon, S., Ramirez, A., & Richards, G. (2020) · Applied Sciences · doi:10.3390/app10186553 · Cited by 56 · see citing works
Véras, M. M. S., Dyer, J.-O., Rooney, M., Silva, P. G. D. B., Rutherford, D. J., & Kairy, D. (2023) · JMIR Research Protocols · doi:10.2196/51873 · Cited by 47 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Lindbäck, Y., Schröder, K., Engström, T., Valeskog, K., & Sonesson, S. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07106-w · Cited by 22 · see citing works
Akinsemolu, A. A., & Onyeaka, H. N. (2025) · Journal of language and Education · doi:10.17323/jle.2025.22118 · Cited by 18 · see citing works
Komolafe, O. O., Mustofa, J., Daley, M., Walton, D. M., & Tawiah, A. K. (2025) · PLoS ONE · doi:10.1371/journal.pone.0325649 · Cited by 13 · see citing works
From Access to Ability: Educator’s Perspectives on AI and New Digital Divides in Higher Education
Hodøl, H.-O., Kjærgård, P. I., & Økland, Ø. (2025) · Digital Society · doi:10.1007/s44206-025-00237-z · Cited by 6 · see citing works
Erem, M., Mercan, O., Yildirim, S., Selçuk, E., & Unyilmaz, C. (2025) · Frontiers in Pediatrics · doi:10.3389/fped.2025.1694035 · Cited by 4 · see citing works
Zhang, M., Liu, T., Peng, X., Chen, Y., & Zhi, M. (2025) · Cureus · doi:10.7759/cureus.89425 · Cited by 3 · see citing works
The use of artificial intelligence in crafting a novel method for teaching normal human gait
Lowe, S. W. (2024) · European Journal of Physiotherapy · doi:10.1080/21679169.2024.2393136 · Cited by 2 · see citing works
Wang, X., Li, M., Du, Y., Yang, H., Ran, J., & Zhang, H. (2026) · Frontiers in Veterinary Science · doi:10.3389/fvets.2026.1746110 · Cited by 1 · see citing works
Ali, S., Basheer, A., Ali, H., Ullah, Z., Lubaba, U., Ahmed, M. W., & Ahmed, A. W. (2026) · The Healer Journal of Physiotherapy and Rehabilitation Sciences · doi:10.55735/57kjc743 · Cited by 1 · see citing works
Sánchez, J. A., Josué, F.-C., Alonso-Pérez, J. L., & Villafañe, J. H. (2026) · doi:10.2196/preprints.91947
Development of Artificial Intelligence (AI) Application for Psychology and Art Therapy
Bando, H., Bando, M., Nishikiori, Y., & Yoshioka, A. (2025) · Journal of Health Care and Research · doi:10.36502/2025/hcr.6251
Dominguez, M., & Lowe, L. M. (2026) · Bulletin of Faculty of Physical Therapy · doi:10.1186/s43161-026-00361-y
- Authors
- Veras, M., Dyer, J.-O., & Kairy, D.
- Affiliations
- Carleton University (CA)
- Venue
- Cureus
- Published
- 2024-01-20
- Volume
- 16
- Issue
- 1
- Pages
- e52617-e52617
- ISSN
- 2168-8184 (ISSN-L)
- PMC
- PMC10875905
- OpenAlex
- W4391051474
- Cited by
- 12
- References
- 5
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 1
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Cureus (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- artificial intelligence in education, physiotherapy education, digital divide, physiotherapy students, personalized learning, educational technology, AI ethics, knowledge gaps
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
