Artificial Intelligence in Medical Education: a Scoping Review of the Evidence for Efficacy and Future Directions
Open accessAbstract
Artificial intelligence (AI) has demonstrated clinical potential, yet its influence on medical education remains limited. This review explores AI applications in medical education, evaluates available evidence and considers future applications. We conducted a scoping review (PubMed, MEDLINE, SCOPUS, Google Scholar; 2010–2022) identifying 42 relevant peer-reviewed articles. Four key themes emerged: surgical skills assessment, radiology training, interactive learning, and text interpretation. Current applications enhance surgical simulation and facilitate interactive learning. These tools may evolve towards comprehensive and individualised educational aids. Despite promising early applications, evidence on educational and clinical outcomes remains limited. Future research should prioritise validated outcomes in larger trials to confirm generalisability and address AI limitations.
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These scoping reviews largely describe broad "AI literacy" themes or program-level content rather than systematically extracting and organizing competency- and learning-objective-level outcomes for undergraduate medical education.
“but these syntheses largely describe broad “AI literacy” themes or program-level content rather than systematically extracting and organizing competency- and learning-objective-level outcomes for undergraduate medical education (UME).”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [26]
The Methods (Inclusion and Exclusion Criteria; page unavailable) exclude surveys of AI understanding and discussions of medical-school curriculum adaptation. The Discussion (Strengths and Limitations; page unavailable) distinguishes this review from broad overviews and says it emphasizes concrete educational outcomes. The review does not systematically organize UME competency- and learning-objective-level outcomes, but describing it as largely about broad AI-literacy themes or program-level content is contrary to its stated scope. (LLM, conf=0.86)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredPrior work has mapped curricula and interventions.
“Cheng et al. and broader scoping work across the medical education continuum (Gordon et al.; Tolentino et al.; Ma et al.; Shaw et al.; Rincón et al.) have articulated high-level domains, mapped curricula and interventions, and emphasized the need for clearer competency frameworks24–27,32,33.”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [26]
The Results section (‘Classifying Emergent Applications of AI in Medical Education’; page not provided) groups the reviewed AI educational interventions into four categories, supporting the intervention-mapping part of the claim. The Methods (‘Inclusion and Exclusion Criteria’; page not provided) explicitly exclude discussions of medical-school curriculum adaptation, so this source does not support the curriculum-mapping part. (LLM, conf=0.87)
Experimental — accuracy not yet measuredPrior work has emphasized the need for clearer competency frameworks.
“Cheng et al. and broader scoping work across the medical education continuum (Gordon et al.; Tolentino et al.; Ma et al.; Shaw et al.; Rincón et al.) have articulated high-level domains, mapped curricula and interventions, and emphasized the need for clearer competency frameworks24–27,32,33.”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [26]
The Results section (‘Classifying Emergent Applications of AI in Medical Education’; page unavailable) identifies gaps in competency-based surgical training, and the Discussion (‘Surgery’; page unavailable) proposes using AI assessment to develop competency minimums. That supports a need for clearer surgical competency standards, but the paper does not call for broader competency frameworks across medical education. (LLM, conf=0.78)
Experimental — accuracy not yet measuredSeveral scoping reviews have examined AI within medical education.
“Several scoping reviews have examined AI within medical education23–28”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [26]
Prior work has articulated high-level domains.
“Cheng et al. and broader scoping work across the medical education continuum (Gordon et al.; Tolentino et al.; Ma et al.; Shaw et al.; Rincón et al.) have articulated high-level domains, mapped curricula and interventions, and emphasized the need for clearer competency frameworks24–27,32,33.”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [26]
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Cites
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 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · cited 1× · see citing works
“There has been broad application of AI in clinical medicine from radiological diagnosis [6], recognition of oph-thalmological conditions [7], identifying dermatological malignancy [8, 9] and accurately predicting clinical deterioration [10].”
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115 · see citing works
Methley, A. M., Campbell, S., Chew-Graham, C., McNally, R., & Cheraghi-Sohi, S. (2014) · BMC Health Services Research · doi:10.1186/s12913-014-0579-0 · Cited by 2,706 · see citing works
Objective structured assessment of technical skill (OSATS) for surgical residents
Martin, J. A., Regehr, G., Reznick, R. K., MacRae, H., Murnaghan, J. J., Hutchison, C., & Brown, M. H. (1997) · British journal of surgery · doi:10.1046/j.1365-2168.1997.02502.x · Cited by 2,549 · see citing works
Gilson, A., Safranek, C. W., Huang, T., Socrates, V., Chi, L., Taylor, R. A., & Chartash, D. (2023) · JMIR Medical Education · doi:10.2196/45312 · Cited by 2,078 · see citing works
Lakhani, P., & Sundaram, B. (2017) · Radiology · doi:10.1148/radiol.2017162326 · Cited by 1,775 · see citing works
Haenssle, H. A., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T., Blum, A., Kalloo, A., Hassen, A. B. H., Thomas, L., Enk, A., et al. (2018) · Annals of Oncology · doi:10.1093/annonc/mdy166 · Cited by 1,571 · see citing works
Eysenbach, G. (2023) · JMIR Medical Education · doi:10.2196/46885 · Cited by 840 · see citing works
An interpretable machine learning model for accurate prediction of sepsis in the ICU
Nemati, S., Holder, A., Razmi, F., Stanley, M. D., Clifford, G. D., & Buchman, T. G. (2018) · Critical Care Medicine · doi:10.1097/ccm.0000000000002936 · Cited by 832 · see citing works
Machine Learning and Health Care Disparities in Dermatology
Adamson, A. S., & Smith, A. (2018) · JAMA Dermatology · doi:10.1001/jamadermatol.2018.2348 · Cited by 639 · cited 1× · see citing works
“For example, AI algorithms for detecting skin cancer are primarily trained on data from light-skinned individuals and may be less likely to recognise melanoma on darker skin [70].”
A scoping review of artificial intelligence in medical education: BEME Guide No. 84
Gordon, M., Daniel, M., Ajiboye, A., Uraiby, H., Xu, N. Y., Bartlett, R., Hanson, J., Haas, M., Spadafore, M., Grafton-Clarke, C., et al. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2314198 · Cited by 543 · cited 1× · see citing works
“For example, Tozsin et al. [72] conducted a systematic review with a search period until March of 2023 and Gordon et al. [73] conducted a scoping review including articles until August 2023.”
A short guide for medical professionals in the era of artificial intelligence
Meskó, B., & Görög, M. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00333-z · Cited by 409 · see citing works
Readiness of US General Surgery Residents for Independent Practice
George, B. C., Bohnen, J. D., Williams, R. G., Meyerson, S. L., Schuller, M. C., Clark, M., Meier, A. H., Torbeck, L. J., Mandell, S. P., Mullen, J. T., et al. (2017) · Annals of Surgery · doi:10.1097/sla.0000000000002414 · Cited by 343 · see citing works
Mirchi, N., Bissonnette, V., Yilmaz, R., Ledwos, N., Winkler-Schwartz, A., & Del Maestro, R. F. (2020) · PLOS ONE · doi:10.1371/journal.pone.0229596 · Cited by 296 · see citing works
Winkler-Schwartz, A., Bissonnette, V., Mirchi, N., Ponnudurai, N., Yilmaz, R., Ledwos, N., Siyar, S., Azarnoush, H., Karlık, B., & Maestro, R. F. D. (2019) · Journal of surgical education · doi:10.1016/j.jsurg.2019.05.015 · Cited by 295 · see citing works
Fazlollahi, A. M., Bakhaidar, M., Alsayegh, A., Yilmaz, R., Winkler-Schwartz, A., Mirchi, N., Langleben, I., Ledwos, N., Sabbagh, A. J., Bajunaid, K., et al. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.49008 · Cited by 285 · see citing works
Scheetz, J., Rothschild, P., McGuinness, M. B., Hadoux, X., Soyer, H. P., Janda, M., Condon, J. J. J., Oakden‐Rayner, L., Palmer, L. J., Keel, S., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-84698-5 · Cited by 277 · see citing works
Wang, Z., & Majewicz Fey, A. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1860-1 · Cited by 275 · see citing works
Artificial Intelligence in Dermatology: A Primer
Young, A. T., Xiong, M., Pfau, J., Keiser, M. J., & Wei, M. L. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.02.026 · Cited by 249 · see citing works
Video-based surgical skill assessment using 3D convolutional neural networks
Funke, I., Mees, S. T., Weitz, J., & Speidel, S. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-01995-1 · Cited by 227 · see citing works
Automated robot‐assisted surgical skill evaluation: Predictive analytics approach
Fard, M. J., Ameri, S., Ellis, R. D., Chinnam, R. B., Pandya, A. K., & Klein, M. D. (2017) · International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.1850 · Cited by 189 · see citing works
Winkler-Schwartz, A., Yilmaz, R., Mirchi, N., Bissonnette, V., Ledwos, N., Siyar, S., Azarnoush, H., Karlık, B., & Maestro, R. F. D. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.8363 · Cited by 170 · see citing works
Medical Student Perspectives on the Impact of Artificial Intelligence on the Practice of Medicine
Park, C. J., Yi, P. H., & Siegel, E. L. (2021) · Current Problems in Diagnostic Radiology · doi:10.1067/j.cpradiol.2020.06.011 · Cited by 167 · see citing works
Evaluation of Deep Learning Models for Identifying Surgical Actions and Measuring Performance
Khalid, S., Goldenberg, M. G., Grantcharov, T., Taati, B., & Rudzicz, F. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.1664 · Cited by 159 · see citing works
Automation of surgical skill assessment using a three-stage machine learning algorithm
Lavanchy, J. L., Zindel, J., Kirtac, K., Twick, I., Hosgor, E., Candinas, D., & Beldi, G. (2021) · Scientific Reports · doi:10.1038/s41598-021-84295-6 · Cited by 153 · see citing works
Artificial Intelligence Distinguishes Surgical Training Levels in a Virtual Reality Spinal Task
Bissonnette, V., Mirchi, N., Ledwos, N., Alsideiri, G., Winkler-Schwartz, A., & Maestro, R. F. D. (2019) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.18.01197 · Cited by 151 · 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
Blumstein, G. W., Zukotynski, B. K., Cevallos, N., Ishmael, C. R., Zoller, S., Burke, Z., Clarkson, S. J., Park, H. E., Bernthal, N. M., & SooHoo, N. F. (2020) · Journal of surgical education · doi:10.1016/j.jsurg.2020.01.002 · Cited by 131 · see citing works
Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-02039-4 · Cited by 126 · see citing works
Artificial intelligence and clinical anatomical education: Promises and perils
Lazarus, M. D., Truong, M., Douglas, P., & Selwyn, N. (2024) · Anatomical Sciences Education · doi:10.1002/ase.2221 · Cited by 116 · see citing works
Advancing Surgical Education: The Use of Artificial Intelligence in Surgical Training
Guerrero, D. T., Asaad, M., Rajesh, A., Hassan, A. M., & Butler, C. E. (2022) · The American Surgeon · doi:10.1177/00031348221101503 · Cited by 114 · see citing works
Intelligent dental training simulator with objective skill assessment and feedback
Rhienmora, P., Haddawy, P., Suebnukarn, S., & Dailey, M. N. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2011.04.003 · Cited by 103 · see citing works
Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions
Kirubarajan, A., Young, D., Khan, S., Crasto, N., Sobel, M., & Sussman, D. (2022) · Journal of Surgical Education · doi:10.1016/j.jsurg.2021.09.012 · Cited by 101 · see citing works
The Role of Artificial Intelligence in Medical Education: A Systematic Review
Tozsin, A., Uçmak, H., Soytürk, S., Aydın, A., Gözen, A. S., Fahim, M. M. A., Güven, S., & Ahmed, K. (2024) · Surgical Innovation · doi:10.1177/15533506241248239 · Cited by 96 · see citing works
Randhawa, G. K., & Jackson, M. (2020) · Healthcare Management Forum · doi:10.1177/0840470419869032 · Cited by 96 · see citing works
Artificial intelligence for healthcare and medical education: a systematic review.
Li, S., Yin, C., Xu, Q., & Zhao, W. (2023) · PubMed Central · Cited by 96 · see citing works
Artificial intelligence-based education assists medical students’ interpretation of hip fracture
Cheng, C.-T., Chen, C.-C., Fu, C.-Y., Chaou, C.-H., Wu, Y.-T., Hsu, C.-P., Chang, C.-C., Chung, I.-F., Hsieh, C.-H., Hsieh, M.-J., et al. (2020) · Insights into Imaging · doi:10.1186/s13244-020-00932-0 · Cited by 87 · see citing works
Modeling Surgical Technical Skill Using Expert Assessment for Automated Computer Rating
Azari, D. P., Frasier, L. L., Quamme, S. R. P., Greenberg, C. C., Pugh, C. M., Greenberg, J. A., & Radwin, R. G. (2019) · Annals of Surgery · doi:10.1097/sla.0000000000002478 · Cited by 84 · see citing works
Kowalewski, K., Garrow, C. R., Schmidt, M. W., Benner, L., Müller-Stich, B. P., & Nickel, F. (2019) · Surgical Endoscopy · doi:10.1007/s00464-019-06667-4 · Cited by 74 · see citing works
Maicher, K. R., Zimmerman, L., Wilcox, B., Liston, B., Cronau, H., Macerollo, A., Jin, L., Jaffe, E., White, M., Fosler-Lussier, E., et al. (2019) · Medical Teacher · doi:10.1080/0142159x.2019.1616683 · Cited by 72 · see citing works
Choi, C. Q. (2021) · IEEE Spectrum · doi:10.1109/mspec.2021.9563958 · Cited by 71 · see citing works
Alonso-Silverio, G. A., Escamirosa, F. P., Bruno-Sanchez, R., Ortiz-Simón, J. L., Muñoz, R., Minor‐Martínez, A., & Alarcón-Paredes, A. (2018) · Surgical Innovation · doi:10.1177/1553350618777045 · Cited by 70 · see citing works
Towards near real-time assessment of surgical skills: A comparison of feature extraction techniques
Anh, N. X., Nataraja, R. M., & Chauhan, S. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105234 · Cited by 66 · see citing works
Yang, Y.-Y., & Shulruf, B. (2019) · Journal of Educational Evaluation for Health Professions · doi:10.3352/jeehp.2019.16.7 · Cited by 65 · see citing works
Artificial Neural Networks to Assess Virtual Reality Anterior Cervical Discectomy Performance
Mirchi, N., Bissonnette, V., Ledwos, N., Winkler-Schwartz, A., Yilmaz, R., Karlık, B., & Maestro, R. F. D. (2019) · Operative Neurosurgery · doi:10.1093/ons/opz359 · Cited by 61 · see citing works
Feasibility of an AI-Based Measure of the Hand Motions of Expert and Novice Surgeons
Uemura, M., Tomikawa, M., Miao, T., Souzaki, R., Ieiri, S., Akahoshi, T., Lefor, A. K., & Hashizume, M. (2018) · Computational and Mathematical Methods in Medicine · doi:10.1155/2018/9873273 · Cited by 59 · see citing works
Using a Machine Learning Architecture to Create an AI-Powered Chatbot for Anatomy Education
Li, Y. S., Lam, C. S. N., & See, C. (2021) · Medical Science Educator · doi:10.1007/s40670-021-01405-9 · Cited by 59 · see citing works
Exploring the roles of artificial intelligence in surgical education: A scoping review
Bilgiç, E., Gorgy, A., Yang, A., Cwintal, M., Ranjbar, H., Kahla, K., Reddy, D. S., Li, K., Ozturk, H., Zimmermann, E., et al. (2021) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2021.11.023 · Cited by 57 · see citing works
Automatically rating trainee skill at a pediatric laparoscopic suturing task
Oquendo, Y. A., Riddle, E. W., Hiller, D. F., Blinman, T. A., & Kuchenbecker, K. J. (2017) · Surgical Endoscopy · doi:10.1007/s00464-017-5873-6 · Cited by 57 · see citing works
Smartpathk: a platform for teaching glomerulopathies using machine learning
Aldeman, N. L. S., Aita, K. M. D. S. U., Machado, V. P., Sousa, L. C. D. D. M., Coelho, A. G. B., da Silva, A. S., Mendes, A. P. D. S., Neres, F. J. D. O., & do Monte, S. J. H. (2021) · BMC Medical Education · doi:10.1186/s12909-021-02680-1 · Cited by 56 · see citing works
Wang, M., Sun, Z., Jia, M., Wang, Y., Wang, H., Zhu, X., Chen, L., & Ji, H. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01797-7 · Cited by 52 · see citing works
Ötleş, E., Kendrick, D. E., Solano, Q. P., Schuller, M. C., Ahle, S. L., Eskender, M. H., Carnes, E., & George, B. C. (2021) · Academic Medicine · doi:10.1097/acm.0000000000004153 · Cited by 48 · see citing works
Davids, J., Makariou, S.-G., Ashrafian, H., Darzi, A. W., Marcus, H. J., & Giannarou, S. (2021) · World Neurosurgery · doi:10.1016/j.wneu.2021.01.117 · Cited by 45 · see citing works
Ledwos, N., Mirchi, N., Yilmaz, R., Winkler-Schwartz, A., Sawni, A., Fazlollahi, A. M., Bissonnette, V., Bajunaid, K., Sabbagh, A. J., & Maestro, R. F. D. (2022) · Journal of neurosurgery · doi:10.3171/2021.12.jns211563 · Cited by 44 · see citing works
Maicher, K. R., Stiff, A., Scholl, M., White, M., Fosler-Lussier, E., Schuler, W., Serai, P., Sunder, V., Forrestal, H., Mendella, L., et al. (2023) · Medical Teacher · doi:10.1080/0142159x.2022.2130216 · Cited by 44 · see citing works
Formative feedback generation in a VR-based dental surgical skill training simulator
Yin, M. S., Haddawy, P., Suebnukarn, S., Kulapichitr, F., Rhienmora, P., Jatuwat, V., & Uthaipattanacheep, N. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103659 · Cited by 39 · see citing works
Development of an intelligent surgical training system for Thoracentesis
Nakawala, H., Ferrigno, G., & Momi, E. D. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.10.004 · Cited by 38 · see citing works
Use of a Machine Learning Algorithm to Classify Expertise
Watson, R. (2014) · Academic Medicine · doi:10.1097/acm.0000000000000316 · Cited by 37 · see citing works
Artificial intelligence and the adoption of new technology in medical education
Webster, C. S. (2020) · Medical Education · doi:10.1111/medu.14409 · Cited by 36 · see citing works
Self-citationMachine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task
Siyar, S., Azarnoush, H., Rashidi, S., Winkler-Schwartz, A., Bissonnette, V., Ponnudurai, N., & Maestro, R. F. D. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02155-3 · Cited by 35 · see citing works
Ludwig, C. A., Perera, C. M., Myung, D. J., Greven, M. A., Smith, S. J., Chang, R. T., & Leng, T. (2020) · Translational Vision Science & Technology · doi:10.1167/tvst.9.2.60 · Cited by 33 · see citing works
Deep Learning-Based Haptic Guidance for Surgical Skills Transfer
Fekri, P., Dargahi, J., & Zadeh, M. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.586707 · Cited by 29 · see citing works
Lee, Y., Kirubarajan, A., Patro, N., Soon, M. S., Doumouras, A. G., & Hong, D. (2020) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2020.11.019 · Cited by 28 · see citing works
Machine Scoring of Medical Students’ Written Clinical Reasoning: Initial Validity Evidence
Cianciolo, A. T., LaVoie, N., & Parker, J. (2021) · Academic Medicine · doi:10.1097/acm.0000000000004010 · Cited by 28 · see citing works
Using Machine Learning to Evaluate Attending Feedback on Resident Performance
Neves, S. E., Chen, M. J., Ku, C. M., Karan, S., DiLorenzo, A. N., Schell, R. M., Lee, D. E., Diachun, C. A. B., Jones, S. B., & Mitchell, J. (2020) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000005265 · Cited by 27 · see citing works
French, A., Lendvay, T. S., Sweet, R. M., & Kowalewski, T. M. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-017-1606-5 · Cited by 27 · see citing works
Reich, A., Mirchi, N., Yilmaz, R., Ledwos, N., Bissonnette, V., Tran, D. H., Winkler-Schwartz, A., Karlık, B., & Maestro, R. F. D. (2022) · Operative Neurosurgery · doi:10.1227/ons.0000000000000173 · Cited by 24 · see citing works
Ruberto, A. J., Rodenburg, D., Ross, K., Sarkar, P., Hungler, P. C., Etemad, A., Howes, D., Clarke, D., McLellan, J., Wilson, D., et al. (2021) · AEM Education and Training · doi:10.1002/aet2.10605 · Cited by 22 · see citing works
Developing an intelligent tutoring system for robotic‐assisted surgery instruction
Julian, D., & Smith, R. (2019) · The International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.2037 · Cited by 19 · see citing works
Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance
Rudie, J. D., Duda, J., Duong, M. T., Chen, P., Xie, L., Kurtz, R. M., Ware, J. B., Choi, J., Mattay, R. R., Botzolakis, E. J., et al. (2021) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-021-00470-1 · Cited by 16 · see citing works
Feedback from Digital Systems Used in Higher Education: An Inquiry into Triggered Emotions
Saplacan, D., Jo, H., & Pajalić, Z. (2018) · Studies in health technology and informatics · doi:10.3233/978-1-61499-923-2-421 · Cited by 16 · see citing works
Dao, L., Harmouch, S. S., Chin, A. S.-L., Dao, T., Thian, Z., Chartrand‐Lefebvre, C., & Cohen, J. (2022) · medRxiv · doi:10.1101/2022.07.11.22277278 · Cited by 6 · see citing works
Flagged first, then by citations
1 of the 74 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [13]Abdellatif H, Al Mushaiqri M, Albalushi H, et al. Teaching, learning and assessing anatomy with Artificial Intelligence: the road to a better future. Int J Environ Res Public Health. 2022;19:14209. 10.3390/ijerph192114209doi:10.3390/ijerph192114209. PMC9656803 36361089 · DOI
- [38]Nguyen XA, Ljuhar D, Pacilli M, et al. Surgical skill levels: classification and analysis using deep neural network model and motion signals. Comput Methods Programs Biomed. 2019;177:1–8. 10.1016/j.cmpb.2019.05.008.oq. 31319938 · DOI
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Advancements in artificial intelligence transforming medical education: a comprehensive overview
Khakpaki, A. (2025) · Medical Education Online · doi:10.1080/10872981.2025.2542807 · Cited by 102 · see citing works
Roveta, A., Castello, L. M., Massarino, C., Francese, A., Ugo, F., & Maconi, A. (2025) · AI · doi:10.3390/ai6090227 · Cited by 42 · see citing works
Kattan, L., Moideen, S., Abdelrahman, A., Khabbaz, S., Ibrahim, A. A. E. W., & Mraiche, F. (2025) · Currents in Pharmacy Teaching and Learning · doi:10.1016/j.cptl.2025.102534 · Cited by 22 · see citing works
Hunt, V. M., Sprehe, L. K., de Lomba, W. C., Gameiro, R. R., Woite, N. L., Wade, C. G., Chen, J. H., Rougas, S. C., Fraser, H. S. F., Sullivan, A. M., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02761-9 · Cited by 10 · see citing works
What Are the Opportunities and Challenges of Using AI in Medical Education in Vietnam?
Nguyen, T. A., Nguyen, T. B., Nguyen, D. C., Vu, A. D., Dang, K. L., Le, N. Q., Ngo, D. A., Nguyen, D. K., Hoang, V. T., & Ngo, T. B. (2025) · JMIR Medical Education · doi:10.2196/77817 · Cited by 11 · see citing works
Mukherjee, D., Sagar, K., Kobialka, R. M., Ghosh, P., Weidmann, M., Savareh, B. A., Joardar, S. N., Truyen, U., Wahed, A. A. E., & Ceruti, A. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1707306 · Cited by 10 · see citing works
Huang, S., Yang, Q., Sun, Y., Chen, S., & Gu, L. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08420-z · Cited by 7 · see citing works
Josephson, C. B., Beniczky, S., Denaxas, S., Ikeda, A., Jehi, L. E., Mwesige, A. K., Jetté, N., Jones, G. D., Ryvlin, P., Sen, A., et al. (2025) · Epilepsia · doi:10.1002/epi.70058 · Cited by 5 · see citing works
Gupta, K., Latinovich, M. F., Latinovich, M. F., Singh, K., Patlas, M. N., & Jajodia, A. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02350-z · Cited by 4 · see citing works
Theoretical evolution of AI in medical education: models, frameworks, and future directions
Abdulrahman, K. A. B., & Hefny, M. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1796632 · Cited by 4 · see citing works
Zand, J., Šuvakov, M., Overgaard, S., Kozikowski, C., Jimison, R. C., Nelson, K. B., Silvano, C. J., Meiners, L. M., Fan, J. W., Schultz, K. E., et al. (2026) · Mayo Clinic proceedings · doi:10.1016/j.mayocp.2025.11.008 · Cited by 3 · see citing works
Gonçalves, N., Collares, C. F., & Pêgo, J. M. (2026) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2025.1735237 · Cited by 3 · see citing works
Artificial intelligence in health professions education: A state-of-the-art meta-reviewPreprint
Hossain, M. M., Hossain, P., Roy, T. J., Das, J., Tasnim, S., Ma, P., & Liaw, W. R. (2025) · medRxiv · doi:10.1101/2025.10.20.25338371 · Cited by 3 · see citing works
Moretti, V., & Miele, F. (2026) · Health Risk & Society · doi:10.1080/13698575.2026.2666555 · Cited by 2 · see citing works
Mondal, H. (2025) · Medical Science Educator · doi:10.1007/s40670-025-02428-2 · Cited by 1 · see citing works
Innovative Approaches to Training in Evidence‐Based Neurology
Raman, A., Ahmed, A. T., Sanghvi, G. V., Ballal, S., Aminov, Z., Al‐Hetty, H. R. A. K., Kalia, R., Arya, R., Kariem, M., & Mustafa, Y. F. (2025) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.70224 · Cited by 1 · see citing works
Chauhan, Galwankar, S., Temple, S., & Boyer, B. (2026) · International Journal of Critical Illness and Injury Science · doi:10.4103/ijciis.ijciis_126_25 · Cited by 1 · see citing works
Zhao, L., Han, Q., Li, P., Dai, H., & Ju, X. (2026) · BMC Medical Education · doi:10.1186/s12909-026-08947-9 · Cited by 1 · see citing works
Hybrid intelligence: understanding how AI reframes risk and uncertainty in dementia care
Moretti, V., Consoloni, M., Miele, F., & Rubini, L. (2026) · Health Risk & Society · doi:10.1080/13698575.2026.2659025 · Cited by 1 · see citing works
Evaluating large language models for pharmacotherapy simulations: a mixed-methods study
Farrag, A. N., El-Zeiny, A., & Ali, A. M. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02626-1 · Cited by 2 · see citing works
Hunt, W., Lock, S., Bonfield, A., & Howick, J. (2026) · BMJ Open · doi:10.1136/bmjopen-2025-115169 · Cited by 1 · see citing works
Wang, Y., & Wang, N. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1847867 · Cited by 1 · see citing works
Zhao, K., Wang, N., & Xie, Z. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1915158 · Cited by 1 · see citing works
Temporal trends of artificial intelligence in medical education: a global perspective
Parente, S. B. M., Rocha, S. S., Moreira, M. E. S., Filho, A. B. D. O., & Simeone, D. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00609-x · Cited by 1 · see citing works
Technology, evidence, and continuing clinical education: a role for self-directed learning?
Webster, C. S., & Weller, J. M. (2025) · British Journal of Anaesthesia · doi:10.1016/j.bja.2025.07.004
Self-citationChen, C., & Li, L. (2025) · Intelligent Information Management · doi:10.4236/iim.2025.176014
AI Performance on Image-based Medical Case Scenarios: A Cross-Sectional Comparative StudyPreprint
Liu, J.-W., Qian, Y., Ma, X., Fan, J., Guo, L.-W., & Yang, H. (2025) · Research Square · doi:10.21203/rs.3.rs-8096665/v1
Applications of artificial intelligence in medical education
Pedro, L. (2025) · International Journal of Clinical Medical Research · doi:10.61466/ijcmr4010003
Safdar, A., Iftikhar, H., Arshad, H. A., Anwar, M., Khan, A. Q., & Kanwal, S. (2025) · VAWKUM Transactions on Computer Sciences · doi:10.21015/vtcs.v13i2.2278
Liu, W.-Z., Lu, K.-N., Yan, Z., & Qian, J.-H. (2026) · Research Square · doi:10.21203/rs.3.rs-7944588/v1
Inteligencia artificial y carga cognitiva en la educación médica
Estrada-Araoz, E. G. (2026) · Educación Médica · doi:10.1016/j.edumed.2026.101159
Kulkarni, A., & Cauley, R. P. (2026) · Aesthetic Plastic Surgery · doi:10.1007/s00266-026-05697-3
Advanced Simulation: The End of the Sandbox
Yassin, A. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1014573
Sepúlveda-Villegas, M., Jiménez-Pérez, M. I., Sánchez-López, A. L., del Campo, K. N. R.-M., Sanchez‐Ante, G., & Navarro-López, D. E. (2025) · International Conference on Artificial Intelligence and Teacher Education (ICAITE) · doi:10.1109/icaite68636.2025.11442596
Personalized Learning in Programming: Evaluating a Chatbot Using Naive Bayes Versus IRT
Morales, M., Silva, J., Cubillos, C., & Mellado, R. (2025) · IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) · doi:10.1109/chilecon66915.2025.11476608
Design and application of a web-based intelligent ophthalmic image analysis teaching platform
Chen, J., Yang, Y., Song, Y., Huang, Z., Li, X., Qu, B., Wan, J., & Zhong, L. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1815698
Yuan, L., Xie, X., & Wen, Y. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09343-z
Kohila, K. R. V. (2025) · IEEE International Conference on Recent Trends in Computing and Smart Mobility (RCSM) · doi:10.1109/rcsm67767.2025.11507307
Glinkowski, W., Jacennik, B., Jankowska, K., Cedro, T., Szymon Wilk, A., & Doniec, R. (2026) · Applied Sciences · doi:10.3390/app16104858 · Cited by 1 · see citing works
Khan, M. M., Shon, R., Yoon, J., Chien, I., Pouraryan, A., Zahraee, S., Mehranpour, P., Chakravarty, N., Immadisetty, S., Ninan, D., et al. (2026) · doi:10.2196/preprints.100784
Kurt, F., & Subaşı, A. (2026) · medRxiv · doi:10.64898/2026.05.29.26354432
Kuzu, T. E., Esen, B. N., & Kurtaran, Z. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09616-7
Shiyi, P., Ruimeng, T., Zhang, H., Hongyun, H., Kou, C., Nie, S., Li, K., Zhongqiong, Z., & Zhihai, L. (2026) · Medical Science Educator · doi:10.1007/s40670-026-02781-w
Zhu-ge, Y., Yao, X.-T., Mei, H., Yao, H.-X., & Chen, Q. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09734-2
Alnazawi, A., & Almarhabi, M. (2026) · Advances in Simulation · doi:10.1186/s41077-026-00447-6 · Cited by 3 · see citing works
Revolutionising Medical Higher Education: A Narrative Review of AI Adoption and Its Implications
Hysaj, A., Khan, S. A., Hamam, D., & Khan, A. A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31418-5_17
Öğüt, E. (2026) · Education and human development · doi:10.5772/intechopen.1014146
Chukwukwe, I. O., Chikezie, T., Ekwufulem, O. N., Chukwuoke, T. V., Uwanuruochi, V., & Chikezie, K. (2026) · EIJRIHS · doi:10.66811/eijrihs.vol1.no3.44
Zahid, K. (2026) · Research Square · doi:10.21203/rs.3.rs-10581991/v1
Cheevaidsarakul, P., & Lertvivatpong, N. (2026) · Journal of Southeast Asian Medical Research · doi:10.55374/jseamed.v10.304
Sonnenberg, L. K., Wiljer, D., Beaudry, K. M., McEwen, D., Haroon, B., Do, V., Tang, B., Mamdani, M., & Maniate, J. M. (2026) · doi:10.2196/preprints.110079
Zahid, K., & Riaz, M. S. (2026) · doi:10.2196/preprints.111382
AI-Aware but AI-Unprepared: The Curriculum Gap at a Health Sciences Higher Institution in UgandaPreprint
SHARIF, K., & Mayimuna, N. (2026) · Research Square · doi:10.21203/rs.3.rs-11092233/v1
Chen, Y., Zhang, S., Liu, X., Li, L., Si, M., & Xu, C. (2026) · Behavioral Sciences · doi:10.3390/bs16101715
Shekera, O. (2026) · Health of Society · doi:10.32782/2306-2436.16.1.2026.1
Stethoscope to algorithm: Artificial intelligence in the advancement of clinical practice
Meo, S. A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45973-3.00008-7
冼琼珍, Chen, S., Bai, Y., Cui, K., Liu, L., Wang, W., Zhou, S., & Zhan, X. (2026) · Animals · doi:10.3390/ani16213379
- Authors
- Shaw, K., Henning, M. A., & Webster, C. S.
- Affiliations
- University of Auckland (NZ)
- Corresponding author
- Shaw, Kody
- Venue
- Medical Science Educator
- Published
- 2025-04-02
- Volume
- 35
- Issue
- 3
- Pages
- 1803-1816
- ISSN
- 2156-8650 (ISSN-L)
- PMC
- PMC12228863
- OpenAlex
- W4409168865
- Cited by
- 58
- References
- 74
- Citations by year
- 2026: 39 · 2025: 19
- FWCI
- 3.43
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Medical Science Educator (publishedVersion, cc-by)
- SSRN Electronic Journal (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ResearchSpace (University of Auckland) (submittedVersion, cc-by)
- Funders
- University of Auckland
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- artificial intelligence, medical education, surgical skill assessment, surgical simulation, radiology education, interactive learning, textual interpretation, individualised learning
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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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