The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Applications of Artificial Intelligence (AI) in Medical Education: A Scoping Review
Open accessAbstract
Artificial Intelligence (AI) is increasingly used to support medical students’ learning journeys, providing personalized experiences and improved outcomes. We conducted a scoping review to explore the current application and classifications of AI in medical education. Following the PRISMA-P guidelines, we searched four databases, ultimately including 22 studies. Our analysis identified four AI methods used in various medical education domains, with the majority of applications found in training labs. The use of AI in medical education has the potential to improve patient outcomes by equipping healthcare professionals with better skills and knowledge. Post-implementation refers to the outcomes of AI-based training, which showed improved practical skills among medical students. This scoping review highlights the need for further research to explore the effectiveness of AI applications in different aspects of medical education.
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“Similar to other fields and industries, AI is widely recognized in healthcare as a ground-breaking technological innovation that leads to better treatment outcomes [2–5].”
From Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey (2024) · cites this paper as [2]
In the citing paper
“Despite its proven benefits, the full integration of AI faces hurdles, with ethical concerns standing out as a key obstacle.”
From Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education (2024) · cites this paper as [4]
In the citing paper
“biases inherent in AI algorithms that could lead to unequal educational opportunities or misinterpretations in clinical training”
From Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education (2024) · cites this paper as [4]
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“AI also raises significant ethical concerns including privacy issues related to the handling and security of sensitive student and patient data”
From Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education (2024) · cites this paper as [4]
In the citing paper
“the lack of transparency in AI decision-making processes making it difficult for users to understand how conclusions are drawn”
From Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education (2024) · cites this paper as [4]
In the citing paper
“the potential devaluation of human judgment as AI becomes more integrated into educational frameworks”
From Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education (2024) · cites this paper as [4]
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Citations
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PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation
Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., et al. (2018) · Annals of Internal Medicine · doi:10.7326/m18-0850 · Cited by 43,565
del Blanco, Á., Torrente, J., Fernández‐Manjón, B., Pérez, P. R., & Giner, M. (2017) · Nurse Education Today · doi:10.1016/j.nedt.2017.04.026 · Cited by 73
Shim, J. S., Kim, J. Y., Pyun, J. H., Cho, S., Oh, M. M., Kang, S. H., Lee, J. G., Kim, J. J., Cheon, J., & Kang, S. G. (2018) · Medicine · doi:10.1097/md.0000000000013569 · Cited by 27
Bouthors, C., Veil, R., Aurégan, J.-C., Molina, V., Blanié, A., Court, C., & Benhamou, D. (2022) · International Orthopaedics · doi:10.1007/s00264-022-05626-4 · Cited by 8
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Brunelli, L. D. L., Fonda, F., & Brusaferro, S. (2026) · JMIR Medical Education · doi:10.2196/92246
Abdulaeva, Z., Abdullaeva, A., Aleksandrov, Y., Anisimov, Y., Arabov, M., Beterbiev, T., Boldyrev, M., Botova, E. D., Vahnenko, N., Vedernikov, V., et al. (2026) · Publishing Center RIOR eBooks · doi:10.29039/02181-1
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Xu, R., Hu, C., & Mu, T. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1805800
Bindi, J. M., Jamali, T., Danze, T., Zabaneh, I. D., Fat, M. N., Tutera, J., & Cox, J. (2026) · JMIR AI · doi:10.2196/74198
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Schliz, K., Rädel-Ablass, K., Schlick, C., Pahr-Hosbach, S., Schwendemann, H., Rupp, S., Roddewig, M., & Miersch, C. (2026) · Springer eBooks · doi:10.1007/978-3-662-72469-9_21
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Wu, H., Liu, Y., Cui, M., Cao, X., & Yuan, Y. (2026) · AJP Advances in Physiology Education · doi:10.1152/advan.00026.2026
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Kundurti, N., & Schweizer, J. (2026) · doi:10.2196/preprints.101964
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Salinas, S. E. B. (2026) · Revista Multidisciplinaria Investigación Contemporánea · doi:10.58995/redlic.rmic.v4.n3.a240
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Zhou, J., Zhang, X., Chen, H., Hu, F., & Zhang, Y. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-0045-0_12
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Çaloğlu, A. S., Durna, H., Ergen, Z. N., & Başer, A. (2026) · Journal of Medical Education and Family Medicine · doi:10.62425/jmefm.1888807
Wiedermann, C. J., Wiedermann, A., & Reismann, H. (2026) · Journal of Medical Education and Family Medicine · doi:10.62425/jmefm.1850362
Анатольевна, Г. Н., Volkova, O. V., Magomedov, S. G., & Bakalov, K. A. (2026) · Epidemiology and Infectious Diseases · doi:10.17816/eid702596
Canbay, F. Ç., Çitil, E. T., & Amanak, K. (2026) · Research Square · doi:10.21203/rs.3.rs-10411366/v1
Li, X., Lu, Y. D., Luo, H. Y., Xiong, Y. X., Li, F., Wang, Q., Cui, Y., Tan, H., Xie, J. P., & Guo, Y. (2026) · Medical Science Educator · doi:10.1007/s40670-026-02859-5
Zhang, C., & Xu, J. (2026) · ANZ Journal of Surgery · doi:10.1111/ans.70972
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Creel, A. M., Grant, L., Ratcliffe, T. A., & Dallaghan, G. L. B. (2026) · Medical Science Educator · doi:10.1007/s40670-026-02900-7
Chai, B. S., Yim, M. M., Ranjbar, D., & Tang, D. T. (2026) · Research Square · doi:10.21203/rs.3.rs-10198678/v1
Qiu, X., Shen, L., Xie, L., & Wang, L. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1980177
Manam, S., Escobar, V., Zimmerman, C., Zimmerman, T., FAKHOURY, P. I., Haddad, C. N., Dalman, D., Roukoz, R., & Dijkstra, P. (2026) · Discover Education · doi:10.1007/s44217-026-02350-8
THE CURRENT STATE AND FUTURE PROSPECTS OF ARTIFICIAL INTELLIGENCE IN DENTISTRY
Yermolaieva, M. V., Удод, О. А., Volosovets, T. M., Surzhanskyi, S., Дікова, І. Г., Вороніна, Г. С., Roman, O., & Kostenko, R. S. (2026) · Eastern Ukrainian Medical Journal · doi:10.21272/eumj.2026;14(3);742-755
Metadata
- Authors
- Nagi, F., Salih, R., Alzubaidi, M., Shah, H., Alam, T., Shah, Z., & Househ, M.
- Affiliations
- Hamad bin Khalifa University (QA)
- Corresponding author
- Househ, Mowafa
- Venue
- Studies in Health Technology and Informatics
- Publisher
- IOS Press
- Published
- 2023-06-29
- Volume
- 305
- Pages
- 648-651
- ISSN
- 0926-9630 (ISSN-L)
- 1879-8365
- OpenAlex
- W4382631926
- Cited by
- 135
- References
- 4
- Citations by year
- 2026: 41 · 2025: 46 · 2024: 44 · 2023: 4
- FWCI
- 48.57
- Type
- book chapter
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Studies in health technology and informatics (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Innovations in Medical Education
- Social Sciences › Education › Artificial Intelligence in Education
- Keywords
- artificial intelligence, medical education, medical students, personalized learning, simulation-based training, practical skills
- MeSH terms
- Artificial Intelligence, Education, Medical, Humans, Students, Medical, Meta-Analysis as Topic
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