ARTIFICIAL INTELLIGENCE IN MEDICAL EDUCATION AND CLINICAL PRACTICE: A NARRATIVE REVIEW
DOI:
https://doi.org/10.31435/ijitss.4(52).2026.6261Keywords:
Artificial Intelligence, Medical Education, Clinical Practice, Healthcare Technology, Machine LearningAbstract
Background: Artificial intelligence is rapidly reshaping medicine, influencing clinicians’ training and patient care. Despite growing enthusiasm for AI-enabled diagnostics and digital learning tools, integration of these technologies into medical education and clinical practice remains uneven.
Objective: This narrative review synthesises recent literature on the role of AI in medical education and clinical practice, mapping current applications, stakeholder perceptions, and the conditions that determine successful adoption.
Methods: Relevant articles were identified through structured searches of PubMed, Scopus, and EMBASE, prioritising literature from the last decade. Empirical studies, methodological reviews, and authoritative commentaries addressing AI, machine learning, or digital health technologies in clinical or educational settings were included and organised thematically.
Findings: AI-powered tools are embedded across medical training, spanning augmented and virtual reality simulations, bedside mobile learning, and a range of clinical specialties, with performance matching expert levels on narrowly defined diagnostic tasks. Stakeholder attitudes are positive but conditional: medical students expect digital health technology to feature in their curricula yet many consider their own competencies inadequate, and very few physicians-in-training report familiarity with AI. Patients express willingness to engage with AI for self-management while preferring human contact in sensitive clinical domains. Persistent barriers remain, including limited availability of curated data, algorithmic bias, difficulties reproducing published models, and underdeveloped regulatory and workflow structures.
Conclusion: AI delivers its greatest value when deployed as a collaborative instrument that augments rather than replaces clinical judgment. Realising this potential depends on sustained investment in digital literacy, explainable systems, clinician involvement in design, and equitable institutional infrastructure.
References
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A., Almohareb, S. N., Aldairem, A., Alrashed, M., Saleh, K. B., Badreldin, H. A., Yami, M. S. A., Harbi, S. A., & Albekairy, A. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education. https://doi.org/10.1186/S12909-023-04698-Z
Boillat, T., Otaki, F., Baghestani, A., Zarnegar, L., & Kellett, C. (2024). A landscape analysis of digital health technology in medical schools: Preparing students for the future of health care. BMC Medical Education. https://doi.org/10.1186/S12909-024-06006-9
Corridon, P. R., Wang, X., Shakeel, A., & Chan, V. (2022). Digital technologies: Advancing individualized treatments through gene and cell therapies, pharmacogenetics, and disease detection and diagnostics. Biomedicines. https://doi.org/10.3390/BIOMEDICINES10102445
Davies, B., Rafique, J., Vincent, T. R., Fairclough, J., Packer, M. H., Vincent, R., & Haq, I. (2012). Mobile medical education (MoMEd) - how mobile information resources contribute to learning for undergraduate clinical students - a mixed methods study https://doi.org/10.1186/1472-6920-12-1
Dhar, P., Rocks, T., Samarasinghe, R. M., Stephenson, G., & Smith, C. M. (2021). Augmented reality in medical education: Students’ experiences and learning outcomes https://doi.org/10.1080/10872981.2021.1953953
Gordon, W. J., Landman, A., Zhang, H., & Bates, D. W. (2020). Beyond validation: Getting health apps into clinical practice https://doi.org/10.1038/S41746-019-0212-Z
Irons, J., Duenser, A., Pickett, T., Haysom, G., & McGrath, M. J. (2026). AI in medical practice: Doctors’ perspective on the benefits, challenges and facilitators of artificial intelligence scribe use. Health and Technology. https://doi.org/10.1007/S12553-025-01042-X
Merlo, E. M., Sparacino, G., Silvestro, O., Giacobello, M. L., Meduri, A., Casciaro, M., Gangemi, S., & Martino, G. D. (2026). The role of artificial intelligence in shaping the doctor–patient relationship: A narrative review. Healthcare. https://doi.org/10.3390/HEALTHCARE14040481
Nağıyeva, G., & Süleymanov, H. (2026). The irreplaceable role of human doctors in the age of artificial intelligence. EuroGlobal Journal of Linguistics and Language Education. https://doi.org/10.69760/EGJLLE.2602028
Scott, I., Carter, S. M., & Coiera, E. (2021). Exploring stakeholder attitudes towards AI in clinical practice. BMJ Health & Care Informatics. https://doi.org/10.1136/BMJHCI-2021-100450
Stroud, C., Onnela, J., & Manji, H. K. (2019). Harnessing digital technology to predict, diagnose, monitor, and develop treatments for brain disorders. Npj Digital Medicine. https://doi.org/10.1038/S41746-019-0123-Z
Ward, J., Gordon, J., Field, M. J., & Lehmann, H. P. (2001). Communication and information technology in medical education https://doi.org/10.1016/S0140-6736(00)04173-8
Wickramasinghe, N., Thompson, B., & Xiao, J. (2021). The opportunities and challenges of digital anatomy for medical sciences: Narrative review https://doi.org/10.2196/34687
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Anna Bociąg, Łukasz Ptach, Rafał Marciniak, Konrad Kowalski, Aleksandra Milewska
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles are published in open-access and licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Hence, authors retain copyright to the content of the articles.
CC BY 4.0 License allows content to be copied, adapted, displayed, distributed, re-published or otherwise re-used for any purpose including for adaptation and commercial use provided the content is attributed.
