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Application of artificial intelligence in medical education: current scenario and future perspectives
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Introduction: Medical education is a lifetime learning process stretching from undergraduate to postgraduate, specialty training, and beyond. It also applies to various healthcare professionals, including doctors, nurses, and other allied healthcare professionals. Therefore, it is essential to acknowledge the immense role of artificial intelligence in medical education in the current era of rapidly growing technology. Methods: High-quality data that met the study objectives were included. In addition, comprehensive investigations on articles available in reputable databases such as PubMed, Research Gate, PubMed central, Web of Science, and Google Scholar were considered for literature review. Results: Artificial intelligence has fixed various issues in education during the last decade, including language processing, reasoning, planning, and cognitive modelling. Conclusion: It can be used in medical education in the following forms: Virtual Inquiry System, Medical Distance Learning and Management, and Recording teaching videos in medical schools. It can also enhance the value of the non-analytical humanistic aspects of medicine. The goal of this review article was to present the implications of AI in medical education, now and in the coming years.
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“Many countries have started implementing a 'dual approval system' for continuing medical education. This has enabled a multi-fold rise in certified professionals [45].”
From Artificial Intelligence Revolutionizing the Field of Medical Education (2023) · cites this paper as [45]
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Li, M., Wang, X., Du, Y., Zhang, H., & Liao, B. (2025) · Frontiers in Veterinary Science · doi:10.3389/fvets.2025.1651179 · Cited by 3
Hawal, M. V., Simon, M., Husain, N. H. A., Al-Harrasi, S. K. R., & Al-Hinai, L. N. S. (2025) · Journal of Medical Education Development · doi:10.61882/edcj.18.3.14 · Cited by 3
Gonçalves, N., Collares, C. F., & Pêgo, J. M. (2026) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2025.1735237 · Cited by 3
Bsisu, I., Alqassieh, R., Aloweidi, A. S., Abu-Humdan, A., Subuh, A., & Masarweh, D. F. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14050447 · Cited by 3
The Art of Oncology Education and Knowledge Sharing—It is All About the “How”
Correia, G. S. D. C., Rosenstein, L. J., Gosain, R., & Manochakian, R. (2025) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-25-471836 · Cited by 3
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Metadata
- Authors
- Mir, M. M.
- Venue
- PubMed
- Publisher
- National Institutes of Health
- Published
- 2023-07-01
- Volume
- 11
- Issue
- 3
- Pages
- 133-140
- PMC
- PMC10352669
- OpenAlex
- W4384819645
- Cited by
- 165
- References
- 14
- Citations by year
- 2026: 27 · 2025: 81 · 2024: 49 · 2023: 8
- FWCI
- 6.57
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- cc-by-nd
- Locations
- PubMed Central (publishedVersion, cc-by-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of Bisha
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Innovations in Medical Education
- Keywords
- artificial intelligence, medical education, distance education, humanistic medicine, language processing, cognitive modeling
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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.
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