Artificial Intelligence developments in medical education: a conceptual and practical framework
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This article was migrated. The article was marked as recommended. As Artificial Intelligence (AI) develops in medicine, there is a greater awareness that medical education may also benefit from AI. Many AI projects are already underway, and many more are still to come. Most medical education administrators and educators are aware of AI, but are not necessarily familiar enough with it to understand the areas of possible application in both using AI in medical education, and also in the content areas that need to be raised with their students. As such, they are at a disadvantage of not understanding the current lie of the land, and are at an even greater disadvantage of not being able to influence and guide future AI projects. This paper attempts to provide an AI conceptual and practical framework for medical education administrators and educators, so that they may have a clearer understanding of the current situation, and may be better placed to guide future AI developments to meet their needs in medical education.
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The review results showed an overall consensus that current knowledge of AI among physicians is at an alarmingly low level and insufficient for future physicians.
“Our results showed an overall consensus that the current knowledge of AI among physicians is at an alarmingly low level and insufficient for future physicians. 55”
From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [55]
Lack of knowledge in AI among medical school faculty is the single most important hindrance to incorporating AI into the curriculum.
“Based on our results of the previous literature, we found that the lack of knowledge in AI among medical school faculty is the single most important hindrance in incorporating it among the curriculum.34,47,49,55”
From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [55]
Currently, the state of AI in medical education is in its infancy and speculative stages.
“Currently, the state of AI in medical education is in its infancy and speculative stages [13-16].”
From Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum (2022) · cites this paper as [15]
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The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423 · see citing works
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & for the PROBAST Group† (2019) · Annals of Internal Medicine · doi:10.7326/m18-1376 · Cited by 2,984 · see citing works
Empathy Decline and Its Reasons: A Systematic Review of Studies With Medical Students and Residents
Neumann, M., Edelhäuser, F., Tauschel, D., Fischer, M. R., Wirtz, M. A., Woopen, C., Haramati, A., & Scheffer, C. (2011) · Academic Medicine · doi:10.1097/acm.0b013e318221e615 · Cited by 1,663 · see citing works
The Devil is in the Third Year: A Longitudinal Study of Erosion of Empathy in Medical School
Hojat, M., Vergare, M. J., Maxwell, K., Brainard, G. C., Herrine, S. K., Isenberg, G. A., Veloski, J. J., & Gonnella, J. S. (2009) · Academic Medicine · doi:10.1097/acm.0b013e3181b17e55 · Cited by 1,630 · see citing works
Nagendran, M., Chen, Y., Lovejoy, C. A., Gordon, A. C., Komorowski, M., Harvey, H., Topol, E. J., Ioannidis, J. P. A., Collins, G. S., & Maruthappu, M. (2020) · BMJ · doi:10.1136/bmj.m689 · Cited by 1,152 · see citing works
Addressing bias in artificial intelligence in health care
Parikh, R. B., Teeple, S., & Navathe, A. S. (2019) · JAMA · doi:10.1001/jama.2019.18058 · Cited by 764 · see citing works
AMEE Guide No. 21: Curriculum mapping: a tool for transparent and authentic teaching and learning
Harden, R. M. (2001) · Medical Teacher · doi:10.1080/01421590120036547 · Cited by 600 · see citing works
How to Read Articles That Use Machine Learning
Liu, Y., Chen, P.-H. C., Krause, J., & Peng, L. H. (2019) · JAMA · doi:10.1001/jama.2019.16489 · Cited by 530 · see citing works
Sit, C., Srinivasan, R., Amlani, A., Muthuswamy, K., Azam, A., Monzon, L., & Poon, D. S. (2020) · Insights into Imaging · doi:10.1186/s13244-019-0830-7 · Cited by 507 · see citing works
Pesapane, F., Volonté, C., Codari, M., & Sardanelli, F. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0645-y · Cited by 479 · see citing works
Artificial intelligence in medical education
Masters, K. (2019) · Medical Teacher · doi:10.1080/0142159x.2019.1595557 · Cited by 469 · see citing works
Self-citationA systematic review of eye tracking research on multimedia learning
Alemdağ, E., & Çağıltay, K. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.06.023 · Cited by 468 · see citing works
Hussain, M., Zhu, W., Zhang, W., & Abidi, S. M. R. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/6347186 · Cited by 382 · see citing works
Salles, A., Evers, K., & Farisco, M. (2020) · AJOB Neuroscience · doi:10.1080/21507740.2020.1740350 · Cited by 335 · see citing works
Machine learning and medical education
Kolachalama, V. B., & Garg, P. S. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0061-1 · Cited by 320 · 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
Characterizing changes in student empathy throughout medical school
Chen, D. C. R., Kirshenbaum, D. S., Yan, J., Kirshenbaum, E., & Aseltine, R. H. (2012) · Medical Teacher · doi:10.3109/0142159x.2012.644600 · Cited by 290 · see citing works
Eye-tracking technology in medical education: A systematic review
Ashraf, H., Sodergren, M. H., Merali, N., Mylonas, G. P., Singh, H., & Darzi, A. W. (2017) · Medical Teacher · doi:10.1080/0142159x.2017.1391373 · Cited by 222 · see citing works
Gijsberts, C. M., Groenewegen, K. A., Hoefer, I. E., Eijkemans, M. J. C., Asselbergs, F. W., Anderson, T. J., Britton, A. R., Dekker, J. M., Engström, G., Evans, G. W., et al. (2015) · PLOS ONE · doi:10.1371/journal.pone.0132321 · Cited by 197 · see citing works
Healthcare 4.0: A review of frontiers in digital health
Jayaraman, P. P., Forkan, A. R. M., Morshed, A., Haghighi, P. D., & Kang, Y. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1350 · Cited by 191 · see citing works
Predicting Instructor Performance Using Data Mining Techniques in Higher Education
Agaoglu, M. (2016) · IEEE Access · doi:10.1109/access.2016.2568756 · Cited by 183 · see citing works
Early detection of university students with potential difficulties
Hoffait, A.-S., & Schyns, M. (2017) · Decision Support Systems · doi:10.1016/j.dss.2017.05.003 · Cited by 158 · see citing works
A Sentiment Analysis System to Improve Teaching and Learning
Rani, S., & Kumar, P. (2017) · Computer · doi:10.1109/mc.2017.133 · Cited by 157 · see citing works
Wu, J. T.-Y., Wong, K. C. L., Gur, Y., Ansari, N., Karargyris, A., Sharma, A., Morris, M. A., Saboury, B., Ahmad, H., Boyko, O. B., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.22779 · Cited by 150 · see citing works
Chi, M., VanLehn, K. A., Litman, D. J., & Jordan, P. (2011) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-010-9093-1 · Cited by 135 · see citing works
Nirschl, J., Janowczyk, A. R., Peyster, E. G., Frank, R., Margulies, K. B., Feldman, M. D., & Madabhushi, A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0192726 · Cited by 133 · see citing works
Where medical education meets artificial intelligence: ‘Does technology care?’
van der Niet, A. G., & Bleakley, A. (2020) · Medical Education · doi:10.1111/medu.14131 · Cited by 128 · see citing works
A Review of Natural Language Processing in Medical Education
Chary, M. A., Parikh, S., Manini, A. F., Boyer, E. W., & Radeous, M. (2018) · Western Journal of Emergency Medicine · doi:10.5811/westjem.2018.11.39725 · Cited by 112 · see citing works
Randhawa, G. K., & Jackson, M. (2020) · Healthcare Management Forum · doi:10.1177/0840470419869032 · Cited by 96 · see citing works
Predicting achievement and providing support before STEM majors begin to fail
Bernacki, M. L., Chavez, M., & Uesbeck, P. M. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103999 · Cited by 88 · see citing works
Straw, I. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101965 · Cited by 86 · see citing works
Topic-based knowledge mining of online student reviews for strategic planning in universities
Srinivas, S., & Rajendran, S. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.06.034 · Cited by 84 · see citing works
Bakshi, S. K., Lin, S. R., Ting, D. S. W., Chiang, M. F., & Chodosh, J. (2020) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2020-316845 · Cited by 81 · see citing works
Automated essay scoring and the future of educational assessment in medical education
Gierl, M. J., Latifi, S., Lai, H., Boulais, A., & De Champlain, A. (2014) · Medical Education · doi:10.1111/medu.12517 · Cited by 80 · 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
Virtual patient simulator for distributed collaborative medical education
Caudell, T. P., Summers, K. L., Holten, J., Hakamata, T., Mowafi, M., Jacobs, J., Lozanoff, B. K., Lozanoff, S., Wilks, D., Keep, M. F., et al. (2003) · The Anatomical Record Part B: The New Anatomist · doi:10.1002/ar.b.10007 · Cited by 61 · 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
The essential skill of ECG interpretation: How do we define and improve competency?
Kashou, A. H., May, A. M., DeSimone, C., & Noseworthy, P. A. (2019) · Postgraduate Medical Journal · doi:10.1136/postgradmedj-2019-137191 · Cited by 54 · see citing works
Can mobile health apps replace GPs? A scoping review of comparisons between mobile apps and GP tasks
Wattanapisit, A., Teo, C. H., Wattanapisit, S., Teoh, E., Woo, W. J., & Ng, C. J. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-1016-4 · Cited by 54 · see citing works
Artificial intelligence-tutoring problem-based learning in ophthalmology clerkship
Wu, D., Xiang, Y., Wu, X., Yu, T., Huang, X., Zou, Y., Liu, Z., & Lin, H. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2019.12.15 · Cited by 51 · see citing works
Inquire Biology: A Textbook that Answers Questions
Chaudhri, V. K., Cheng, B. H., Overholtzer, A., Roschelle, J., Spaulding, A., Clark, P. E., Greaves, M., & Gunning, D. (2013) · AI Magazine · doi:10.1609/aimag.v34i3.2486 · Cited by 51 · see citing works
University Chatbot using Artificial Intelligence Markup Language
Khin, N. N., & Soe, K. M. (2020) · IEEE Conference on Computer Applications (ICCA) · doi:10.1109/icca49400.2020.9022814 · Cited by 50 · see citing works
The potential of latent semantic analysis for machine grading of clinical case summaries
Kintsch, W. (2002) · Journal of Biomedical Informatics · doi:10.1016/s1532-0464(02)00004-7 · Cited by 44 · see citing works
Automatic argument assessment of final project reports of computer engineering students
García‐Gorrostieta, J. M., López‐López, A., & González‐López, S. (2018) · Computer Applications in Engineering Education · doi:10.1002/cae.21996 · Cited by 42 · see citing works
Preparing medical students for the e-patient
Masters, K. (2017) · Medical Teacher · doi:10.1080/0142159x.2017.1324142 · Cited by 37 · see citing works
Self-citationAçıkkar, M., & Akay, M. F. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.09.007 · Cited by 33 · see citing works
An adaptive Student Centered Curriculum for an intelligent training system
Eliot, C., & Woolf, B. P. (1995) · User Modeling and User-Adapted Interaction · doi:10.1007/bf01101802 · Cited by 32 · see citing works
(2000) · PubMed · Cited by 28 · see citing works
Learning from a Computer Tutor with Natural Language Capabilities
MICHAEL, J. A., Rovick, A. A., Glass, M., Zhou, Y., & Evens, M. W. (2003) · Interactive Learning Environments · doi:10.1076/ilee.11.3.233.16543 · Cited by 28 · see citing works
Latifi, S., Gierl, M. J., Boulais, A.-P., & Champlain, A. F. D. (2015) · Evaluation & the Health Professions · doi:10.1177/0163278715605358 · Cited by 27 · see citing works
Support Vector Machine for Spatial Variation
Andris, C., Cowen, D., & Wittenbach, J. D. (2012) · Transactions in GIS · doi:10.1111/j.1467-9671.2012.01354.x · Cited by 27 · see citing works
McFadden, P., & Crim, A. (2016) · Journal of Continuing Education in the Health Professions · doi:10.1097/ceh.0000000000000061 · Cited by 26 · see citing works
Intelligent training system integrated in an echocardiography simulator
Weidenbach, M., Trochim, S., Kreutter, S., Richter, C., Berlage, T., & Grunst, G. (2003) · Computers in Biology and Medicine · doi:10.1016/s0010-4825(03)00084-2 · Cited by 25 · see citing works
Jindal, A., & Bansal, M. (2020) · MedEdPublish · doi:10.15694/mep.2020.000200.1 · Cited by 25 · see citing works
Of Students Academic Performance Rates Using Artificial Neural Networks (ANNs)
Asogwa, O. C., & V.Oladugba, A. (2015) · American Journal of Applied Mathematics and Statistics · doi:10.12691/ajams-3-4-3 · Cited by 24 · see citing works
Entezarjou, A., Bonamy, A. E., Benjaminsson, S., Herman, P. A., & Midlöv, P. (2020) · JMIR Medical Informatics · doi:10.2196/18930 · Cited by 22 · see citing works
Virtual patients in problem‐based learning
Hamdy, H., Al-Moslih, A., Tavarnesi, G., & Laus, A. (2017) · Medical Education · doi:10.1111/medu.13293 · Cited by 19 · see citing works
Ullah, F., Sepasgozar, S. M. E., Tahmasebinia, F., Sepasgozar, S. M. E., & Davis, S. M. (2020) · Construction Economics and Building · doi:10.5130/ajceb.v20i3.7056 · Cited by 17 · see citing works
Parapadakis, D. (2020) · London Review of Education · doi:10.14324/lre.18.2.03 · Cited by 14 · see citing works
Treceño-Fernández, D., Calabia-del-Campo, J., Bote-Lorenzo, M. L., Gómez-Sánchez, E., Luis-García, R. D., & Alberola-López, C. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105634 · Cited by 12 · see citing works
A Decision Support System for Course Offering in Online Higher Education Institutes
Kardan, A., & Sadeghi, H. (2013) · International Journal of Computational Intelligence Systems · doi:10.1080/18756891.2013.808428 · Cited by 12 · see citing works
AI Medical School Tutor: Modelling and Implementation
Afzal, S., Dhamecha, T. I., Gagnon, P., Nayak, A., Shah, A., Carlstedt-Duke, J., Pathak, S., Mondal, S., Gugnani, A., Zary, N., et al. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59137-3_13 · Cited by 10 · see citing works
Hayasaka, Y., Fujikura, T., & Kashimura, M. (2018) · Journal of Nippon Medical School · doi:10.1272/jnms.jnms.2018_85-57 · Cited by 9 · see citing works
The CARDIO-LOGOS system for ECG training and diagnosis
Bourlas, P., Giakoumakis, E. A., Koutsouris, D., Papakonstantinou, G., & Tsanakas, P. D. (1996) · Technology and Health Care · doi:10.3233/thc-1996-3409 · Cited by 5 · see citing works
Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary Algorithm
Zhang, J., Zhang, P., & Xu, B. (2019) · Complexity · doi:10.1155/2019/1712569 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 18
- Alban M. and Mauricio D. (2019) Neural Networks to Predict Dropout at the Universities. International Journal of Machine Learning and Computing. 9(2), pp. 149– 153. 10.18178/ijmlc.2019.9.2.779 · DOI
- Babić I. Đ. (2017) Machine learning methods in predicting the student academic motivation. Croatian Operational Research Review. 8, pp. 443– 461.
- Baker M. Harrison I. and Muir Gray. (2006) Safer IT in a safer NHS: account of a partnership. The British Journal of Healthcare Computing & Information Management. 23(7), pp. 11– 14.
- Becker H. S. and Geer B. (1958) The Fate of Idealism in Medical School. American Sociological Review. 23(1), p. 50. 10.2307/2088623 · DOI
- Billinghurst M. and Savage J. (1996) Adding intelligence to the interface.in Virtual Reality Annual International Symposium. Santa Clara, CA: IEEE, pp. 168– 175.
- Chen C.-K. (2010) Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. 29, pp. 1– 23.
- Clarke A. C. (1980) Electronic Tutors. OMNI. 2(9), pp. 76– 96.
- Jović J. Milić M. Cvetanović S. and Chandra K. (2019) Implementation of machine learning based methods in elearning systems.in The 10th International Conference on eLearning (eLearning-2019), 26-27 September. Belgrade, Serbia, pp. 39– 44.
- Laleye F. de Chalendar G. Blanié A. Brouquet A. (2020) A French Medical Conversations Corpus Annotated for a Virtual Patient Dialogue System.in Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), Marseille, 11-16 May 2020. European Language Resources Association, pp. 574– 580.
- Macrae H. Cohen M. Hashimoto D. and Miller B. (2020) Innovation in Surgical Education Technologies: COVID-19 and Beyond.in AMEE 2020: The Virtual Conference. AMEE 2020. Glasgow, Scotland: AMEE.
- Mosier K. L. and Skitka L. J. (1996) Human Decision Makers and Automated Decision Aids: Made for Each Other?in Parasuraman R. and Mouloua M. (eds) Automation and Human Performance: Theory and Applications. Mahwah, New Jersey: Lawrence Erlbaum, pp. 201– 220.
- National Research Council (2015) Training Students to Extract Value from Big Data: Summary of a Workshop. Washington, DC: The National Academies Press. 10.17226/18981 26065052 · DOI
- Oancea B. Dragoescu R. and Ciucu S. (2013) Predicting students’ results in higher education using neural networks.in International Conference on Applied Information and Communication Technology. Jelgava, Latvia, pp. 190– 193.
- Sharples M. Jeffery N. Teather D. Teather B. (1997) A socio-cognitive engineering approach to the development of a knowledge-based training system for neuroradiology.in Du Boulay B. and Mizoguchi R. (eds) Artificial Intelligence in Education: Knowledge and Media in Learning Systems. Proceedings of AI-ED 97, 8th world conference, Kobe, Japan, August 18-22, 1997. Kobe, Japan: IOS Press, pp. 402– 409.
- Skorikov M. and Momen S. (2020) Machine learning approach to predicting the acceptance of academic papers.in The 2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), Bali, July 7-9, 2020. Bali: IEEE, pp. 113– 117.
- Stasiu R. K. Britto J. de Dias J. da S. and Scalabrin E. (2001) Electrocardiogram Interpretation Guided by a Tutorial Expert.in 14th IEEE Symposium on Computer-Based Medical Systems. Bethesda, MD: IEEE, pp. 487– 492.
- Toivonen T. Jormanainen I. and Tukiainen M. (2019) Augmented intelligence in educational data mining. Smart Learning Environments. 6(1), p. 10. 10.1186/s40561-019-0086-1 · DOI
- Wijayarathna G. K. and Zary N. (2019) Feasibility in using de-identified patient data to enrich artificial applications in medical education. EDULEARN19 Proceedings. 1(July), pp. 7598– 7604. 10.21125/edulearn.2019.1837 · DOI
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Masters, K. (2023) · Medical Teacher · doi:10.1080/0142159x.2023.2186203 · Cited by 302 · see citing works
Self-citationBisdas, S., Topriceanu, C.-C., Zakrzewska, Z., Irimia, A.-V., Shakallis, L., Subhash, J., Casapu, M.-M., Leon-Rojas, J., Pinto dos Santos, D., Andrews, D. M., et al. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.795284 · Cited by 232 · see citing works
Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum
Grunhut, J., Marques, O., & Wyatt, A. T. M. (2022) · JMIR Medical Education · doi:10.2196/35587 · Cited by 166 · see citing works
Tolentino, R., Baradaran, A., Gore, G., Pluye, P., & Abbasgholizadeh-Rahimi, S. (2024) · JMIR Medical Education · doi:10.2196/54793 · Cited by 165 · see citing works
Grunhut, J., Wyatt, A. T., & Marques, O. (2021) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205211036836 · Cited by 161 · see citing works
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BackgroundZahlan, A., Ranjan, R. P., & Hayes, D. (2023) · Technology in Society · doi:10.1016/j.techsoc.2023.102321 · Cited by 166 · see citing works
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AlZaabi, A., AlMaskari, S., & AalAbdulsalam, A. (2023) · DIGITAL HEALTH · doi:10.1177/20552076231152167 · Cited by 93 · see citing works
Masters, K., Herrmann-Werner, A., Festl-Wietek, T., & Taylor, D. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2387802 · Cited by 46 · see citing works
Self-citationSharma, V., Saini, U., Pareek, V., Sharma, L., & Kumar, S. (2023) · Scripta Medica · doi:10.5937/scriptamed54-46267 · Cited by 27 · see citing works
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Alzaabi, A. A., & Masters, K. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07008-x · Cited by 17 · see citing works
Self-citationTolentino, R., Hersson-Edery, F., Yaffe⃰, M. J., & Rahimi, S. A. (2025) · JMIR Medical Education · doi:10.2196/66828 · Cited by 21 · see citing works
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Radhwi, O., & Khafaji, M. A. (2024) · Journal of Family and Community Medicine · doi:10.4103/jfcm.jfcm_144_24 · Cited by 8 · see citing works
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Raskob, K., Duman, H., Kinder, J., Lee, J., Wilson, J., & Segerson, K. E. (2025) · Medical Teacher · doi:10.1080/0142159x.2025.2513427 · Cited by 7 · see citing works
Ahmed, M. M., Huang, F., & Chow, C.-M. (2025) · CJC Open · doi:10.1016/j.cjco.2025.03.013 · Cited by 6 · see citing works
Baigi, S. F. M., Aval, R. N., Sarbaz, M., Tabatabaei, S. M., & Kimiafar, K. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08328-8 · Cited by 5 · see citing works
Insights Gained from Using AI to Produce Cases for Problem-Based Learning
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Costa-Dookhan, K. A., Adirim, Z. L., Maslej, M. M., Donner, K., Rodak, T., Soklaridis, S., Sockalingam, S., & Thakur, A. (2025) · Academic Medicine · doi:10.1097/acm.0000000000005983 · Cited by 4 · see citing works
Nawaz, F. A., Opriessnig, E., Usman, F. M., Agrohi, J., Arshad, Z., Kashyap, R., & Anwar, S. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-4422-4.ch004 · Cited by 3 · see citing works
Chu, L. F., & Kurup, V. (2025) · Current anesthesiology reports · doi:10.1007/s40140-024-00660-x · Cited by 2 · see citing works
Empowering Healthcare Professionals Through AI-Powered Lifelong Learning for Improving Patient Care
Rajendran, R., Subramanian, Y. R., Poddar, S., & Geetha, V. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2440-0.ch006 · Cited by 1 · see citing works
Artificial Intelligence in Critical Care: Promise, Peril, and the Path Forward
Kumar, A., Taneja, A., Singh, Y. P., Singh, G. P., Jain, S., & Meena, S. (2025) · Cureus · doi:10.7759/cureus.85323 · Cited by 1 · see citing works
Promoting Hospitals’ Reputation through Social Media and Artificial Intelligence
Aguerrebere, P. M., Medina, E., & Pacanowski, T. G. (2022) · UNIE Business Research · doi:10.48132/uniebr.377
Dai, Z., Chen, Z., Weng, L., Guo, L., & Dai, Z. (2026) · Updates in Surgery · doi:10.1007/s13304-026-02683-x · Cited by 1 · see citing works
Khan, S. M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44197-4.00015-1
Synthesising Generative Artificial Intelligence Competency Domains for Medical Education Faculty
Meyers, P., & Miller, C. (2026) · The Clinical Teacher · doi:10.1111/tct.70532 · Cited by 1 · see citing works
- Authors
- Masters, K.
- Affiliations
- Sultan Qaboos University (OM)
- Corresponding author
- Masters, Ken
- ORCID
- Masters, Ken
- Venue
- MedEdPublish
- Publisher
- F1000 Research Ltd
- Published
- 2020
- Volume
- 9
- Issue
- 1
- Pages
- 239-239
- ISSN
- 2312-7996 (ISSN-L)
- PMC
- PMC10697470
- OpenAlex
- W3095977225
- Cited by
- 25
- References
- 45
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 5 · 2023: 4 · 2022: 2 · 2021: 2
- FWCI
- 0.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- MedEdPublish (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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, medical education, artificial intelligence in education, artificial intelligence in healthcare
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