Preparing for Artificial General Intelligence (AGI) in Health Professions Education: AMEE Guide No. 172
Abstract
Generative Artificial Intelligence (GenAI) caught Health Professions Education (HPE) institutions off-guard, and they are currently adjusting to a changed educational environment. On the horizon, however, is Artificial General Intelligence (AGI) which promises to be an even greater leap and challenge. This Guide begins by explaining the context and nature of AGI, including its characteristics of multi-modality, generality, adaptability, autonomy, and learning ability. It then explores the implications of AGI on students (including personalised learning and electronic tutors) and HPE institutions, and considers some of the context provided by AGI in healthcare. It then raises the problems to address, including the impact on employment, social risks, student adaptability, costs, quality, and others. After considering a possible timeline, the Guide then ends by indicating some first steps that HPE institutions and educators can take to prepare for AGI.
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Cites
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861 · see citing works
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290 · see citing works
The Singularity Is near: When Humans Transcend Biology
Mead, W. R., & Kurzweil, R. (2006) · Foreign Affairs · doi:10.2307/20031996 · Cited by 3,418 · see citing works
Foundation models for generalist medical artificial intelligence
Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023) · Nature · doi:10.1038/s41586-023-05881-4 · Cited by 2,028 · cited 1× · see citing works
“Other initiatives, such as by Moor et al. [56]) and Google continue to explore the use of multi-modal data ( ‘ like radiology images, lab results, genomics data, environmental context and more ’ ) [57].”
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 5× · see citing works
“…currently still so focused on formulating and implementing responses to GenAI, and the relevant scholarship is so focused on using GenAI in teaching and assessment [2,15,16], that we are ignoring a new, and more powerful wave that is about to break upon us: Artificial General Intelligence (AGI).”
InfluentialShanahan, M. (2015) · The MIT Press eBooks · doi:10.7551/mitpress/10058.001.0001 · Cited by 396 · see citing works
Ethical use of Artificial Intelligence in Health Professions Education: AMEE Guide No. 158
Masters, K. (2023) · Medical Teacher · doi:10.1080/0142159x.2023.2186203 · Cited by 302 · see citing works
Self-citationLa nueva realidad de la educación ante los avances de la inteligencia artificial generativa
García Peñalvo, F. J., Llorens-Largo, F., & Vidal, J. (2023) · RIED-Revista Iberoamericana de Educación a Distancia · doi:10.5944/ried.27.1.37716 · Cited by 238 · see citing works
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
Butlin, P., Long, R., Elmoznino, E., Bengio, Y., Birch, J., Constant, A., Deane, G., Fleming, S. M., Frith, C., Ji, X., et al. (2023) · arXiv · doi:10.48550/arxiv.2308.08708 · Cited by 196 · see citing works
Ulam, S. M. (1958) · Bulletin of the American Mathematical Society · doi:10.1090/s0002-9904-1958-10189-5 · Cited by 189 · see citing works
Xu, X., Chen, Y., & Miao, J. (2024) · Journal of Educational Evaluation for Health Professions · doi:10.3352/jeehp.2024.21.6 · Cited by 169 · see citing works
Kraljevic, Z., Bean, D., Shek, A., Bendayan, R., Hemingway, H., Yeung, J. A., Deng, A., Balston, A., Ross, J., Idowu, E., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00025-6 · Cited by 118 · cited 2× · see citing works
“In the BioPsychoSocial model of patient care, there is even more to process, and all that will need to be included [59].”
The General Factor of Intelligence: How General Is It?
Bates, T. C. (2002) · Intelligence · doi:10.1016/s0160-2896(02)00144-7 · Cited by 118 · see citing works
Lin, C.-S., Liu, W.-T., Tsai, D.-J., Lou, Y.-S., Chang, C.-H., Lee, C.-C., Fang, W.-H., Wang, C.-C., Chen, Y.-Y., Lin, W.-S., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02961-4 · Cited by 115 · see citing works
Advancing the science and practice of precision education to enhance student outcomes
Cook, C. R., Kilgus, S. P., & Burns, M. K. (2017) · Journal of School Psychology · doi:10.1016/j.jsp.2017.11.004 · Cited by 94 · see citing works
Vinge, V. (2013) · doi:10.1002/9781118555927.ch35 · Cited by 59 · see citing works
Bainbridge, W. S. (2006) · Kluwer Academic Publishers eBooks · doi:10.1007/1-4020-4107-1_14 · Cited by 59 · see citing works
Rise of artificial general intelligence: risks and opportunities
Buttazzo, G. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1226990 · Cited by 58 · see citing works
Twelve tips on creating and using custom GPTs to enhance health professions education
Masters, K., Benjamin, J. C., Agrawal, A., MacNeill, H., Pillow, M. T., & Mehta, N. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2305365 · Cited by 56 · see citing works
Self-citationKıyak, Y. S., & Kononowicz, A. A. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2314723 · Cited by 53 · see citing works
Rosenberg, G. S., Magnéli, M., Barle, N., Kontakis, M. G., Müller, A. M., Wittauer, M., Gordon, M., & Brodén, C. (2024) · Acta Orthopaedica · doi:10.2340/17453674.2024.40182 · Cited by 50 · see citing works
Is ChatGPT Making Scientists Hyper-Productive? The Highs and Lows of Using AI
Prillaman, M. (2024) · Nature · doi:10.1038/d41586-024-00592-w · Cited by 41 · see citing works
Essentials of General Intelligence: The Direct Path to Artificial General Intelligence
Voss, P. (2007) · Cognitive technologies · doi:10.1007/978-3-540-68677-4_4 · Cited by 41 · see citing works
Cornwall, J., Hildebrandt, S., Champney, T. H., & Goodman, K. (2024) · Anatomical Sciences Education · doi:10.1002/ase.2335 · Cited by 35 · see citing works
Artificial general intelligence for radiation oncology
Liu, C., Liu, Z., Holmes, J., Zhang, L., Zhang, L., Ding, Y., Shu, P., Wu, Z., Dai, H., Li, Y., et al. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100045 · Cited by 34 · see citing works
Masters, K., & Salcedo, D. (2024) · Medical Teacher · doi:10.1080/0142159x.2023.2298756 · Cited by 26 · see citing works
Self-citationOn the computational complexity of ethics: moral tractability for minds and machines
Stenseke, J. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10732-3 · Cited by 26 · see citing works
Welcoming the Era of Gene Editing in Medicine
Daley, G. Q. (2024) · New England Journal of Medicine · doi:10.1056/nejmp2314279 · Cited by 25 · see citing works
Artificial Intelligence developments in medical education: a conceptual and practical framework
Masters, K. (2020) · MedEdPublish · doi:10.15694/mep.2020.000239.1 · Cited by 25 · see citing works
Self-citationOn the Horizon: Interactive and Compositional Deepfakes
Horvitz, E. (2022) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3536221.3558175 · Cited by 22 · see citing works
‘ChatGPT for CRISPR’ creates new gene-editing tools
Callaway, E. (2024) · Nature · doi:10.1038/d41586-024-01243-w · Cited by 22 · see citing works
Sibbald, M., Zwaan, L., Yılmaz, Y., & Lal, S. (2022) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.13730 · Cited by 20 · see citing works
Becoming adaptive experts in an era of rapid advances in generative artificial intelligence
Hess, B., Cupido, N., Ross, S., & Kvern, B. L. (2023) · Medical Teacher · doi:10.1080/0142159x.2023.2289844 · Cited by 19 · see citing works
Legally Human? ‘Novel Beings’ and English Law
Lawrence, D. R., & Brazier, M. (2018) · Medical Law Review · doi:10.1093/medlaw/fwy017 · Cited by 16 · see citing works
Implications of conscious AI in primary healthcare
Ranjbari, D., & Rahimi, S. A. (2024) · Family Medicine and Community Health · doi:10.1136/fmch-2023-002625 · Cited by 11 · see citing works
Augmenting Human Selves Through Artificial Agents – Lessons From the Brain
Northoff, G., Fraser, M., Griffiths, J. D., Pinotsis, D. A., Panangaden, P., Moran, R., & Friston, K. J. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.892354 · Cited by 9 · see citing works
Wojda, T. R., Hoffman, C., Kindler, K., Desai, A., & Visweswaran, S. (2024) · Artificial intelligence · doi:10.5772/intechopen.114907 · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 38 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Feigerlova, E., Hani, H., & Hothersall-Davies, E. (2025) · BMC Medical Education · doi:10.1186/s12909-025-06719-5 · Cited by 166 · see citing works
Artificial Intelligence in Health Professions Education assessment: AMEE Guide No. 178
Masters, K., MacNeil, H., Benjamin, J., Carver, T., Nemethy, K., Valanci-Aroesty, S., Taylor, D. C. M., Thoma, B., & Thesen, T. (2025) · Medical Teacher · doi:10.1080/0142159x.2024.2445037 · Cited by 80 · see citing works
Self-citationJu, H., Park, M., Jeong, H., Lee, Y., Kim, H., Seong, M., & Lee, D. (2025) · Healthcare Informatics Research · doi:10.4258/hir.2025.31.2.156 · Cited by 27 · see citing works
Artificial General Intelligence: Advancements, Challenges, and Future Directions in AGI Research
Yenduri, G., Murugan, R., Maddikunta, P. K. R., Bhattacharya, S., Sudheer, D., & Bhushan, B. (2025) · IEEE Access · doi:10.1109/access.2025.3592708 · Cited by 30 · see citing works
Transforming clinical reasoning—the role of AI in supporting human cognitive limitations
Greengrass, C. J. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1715440 · Cited by 24 · see citing works
Navigating the AI Revolution in Medicine—Adopting Strategies for Medical Education
Greengrass, C. J. (2024) · Medical Science Educator · doi:10.1007/s40670-024-02257-9 · Cited by 15 · see citing works
The performance of ChatGPT on medical image-based assessments and implications for medical education
Yang, X., & Chen, W. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07752-0 · Cited by 13 · see citing works
The curriculum: The heart and soul of a medical school
Harden, R. M., Lilley, P., & Yıldız, S. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2416307 · Cited by 12 · see citing works
Applications of artificial intelligence in anesthesiology
Shu, X., Zhu, Y., Liu, X., Li, Y., Yi, B., & Wang, Y. (2025) · Anesthesiology and Perioperative Science · doi:10.1007/s44254-025-00131-4 · Cited by 12 · see citing works
Basil, M., Ahmed, W., Hajeomar, R., Strawbridge, J., Lynch, M. R., & Mukhalalati, B. (2026) · BMC Medical Education · doi:10.1186/s12909-025-08527-3 · Cited by 13 · see citing works
Generative AI in Healthcare: Insights from Health Professions Educators and Students
Dong, C., Aw, D. C., Lee, W. C., Low, S. C., & Yan, C. C. K. (2025) · International Medical Education · doi:10.3390/ime4020011 · Cited by 8 · see citing works
Artificial General Intelligence and the Social Contract: A Dynamic Political Economy Model
Stiefenhofer, P. (2025) · Journal of Economic Analysis · doi:10.58567/jea04030007 · Cited by 7 · see citing works
AGI and LLM-Driven Spectrum Intelligence in Future Wireless Networks
Javaid, S., Khan, N., Alwarafy, A., & Saeed, N. (2025) · IEEE Wireless Communications · doi:10.1109/mwc.2025.3600789 · Cited by 7 · see citing works
AI in Psychiatric Education and Training From 2016 to 2024: Scoping Review of Trends
Weightman, M. J., Chur‐Hansen, A., & Clark, S. R. (2025) · JMIR Medical Education · doi:10.2196/81517 · Cited by 8 · see citing works
Key Insights for the Ethical and Appropriate Use of Artificial Intelligence by Medical Learners
Murray, B. P., Thota, D., Baker, C. K., & Stierwalt, J. B. (2024) · Military Medicine · doi:10.1093/milmed/usae536 · Cited by 10 · see citing works
Faculty development through higher degrees: AMEE Guide No. 180
Al-Eraky, M. M., & Taylor, D. (2025) · Medical Teacher · doi:10.1080/0142159x.2025.2458293 · Cited by 5 · see citing works
Self-citationJing, Z., & Mouhong, Z. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08289-y · Cited by 4 · see citing works
“Our findings strongly aligned with calls to incorporate artificial intelligence into medical education systems [41–44].”
Tezcan, H., Tunçez, A., Gürses, K. M., Özen, Y., & Yalçın, M. U. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09141-7 · Cited by 4 · see citing works
Henry, J. A. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1496948 · Cited by 4 · see citing works
Ng, O., Lim, S. Y., Goh, N., Ho, B., Cleland, J., Samuel, A., & Han, S. P. (2026) · Medical Teacher · doi:10.1080/0142159x.2026.2667280 · Cited by 4 · see citing works
IAMSE Artificial Intelligence Meeting Survey: AI’s Impact on Medical Education Faculty
McKell, D., McCoy, L., & Niño, D. F. (2024) · Medical Science Educator · doi:10.1007/s40670-024-02163-0 · Cited by 3 · see citing works
The Future of Artificial Intelligence in Medical Education and Continuing Medical Education
Webber, C. J., Whitworth, P., & Stenner, S. P. (2025) · Primary Care Clinics in Office Practice · doi:10.1016/j.pop.2025.07.010 · Cited by 2 · see citing works
Beyond Scarcity: Reimagining an Abundant Future for Physical Therapist Education
Gagnon, K., & Blackinton, M. T. (2025) · Journal of Physical Therapy Education · doi:10.1097/jte.0000000000000453 · Cited by 2 · see citing works
Use of Generative AI by Veterinary Students in Two UK Vet Schools: A Moment in Time
Rhind, S. M., Connelly, L., Baillie, S., Shaw, D. J., & Dickson, J. (2026) · Journal of Veterinary Medical Education · doi:10.3138/jvme-2025-0078 · Cited by 2 · see citing works
Generative AI Research in Health Professions Education: A Scoping Review
Edara, S. R., Highum, B., Damania, R. C., Hakimzadeh, N., Awasthi, R., Mishra, S., Kashyap, R., Ramachandran, S. P., Bhattacharyya, A., Khare, A., et al. (2026) · Medical Science Educator · doi:10.1007/s40670-026-02810-8 · Cited by 2 · see citing works
Agarwal, G. G., Yenawine, P., Manohar, S., & Chisolm, M. S. (2025) · Medical Teacher · doi:10.1080/0142159x.2025.2458287 · Cited by 2 · see citing works
AI in Healthcare: Do Not Forget About Allied Healthcare
Hulsen, T., & Scheper, M. C. (2025) · AI · doi:10.3390/ai6060114 · Cited by 1 · see citing works
Ultrasound-based artificial intelligence for breast lesion classification
Ma, T., Wang, Z., Dong, J., Cheng, Y., Zhao, H., & Cui, X. W. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1759194 · Cited by 2 · see citing works
Ronald M. Harden retiring as Editor-in-Chief of Medical Teacher
Harden, R. M., & Lilley, P. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2442877
Faculty should be cautiously optimistic about the role of generative AI in medical education
Ye, H. (2025) · Journal of Investigative Medicine · doi:10.1177/10815589251327523
Technology-enhanced learning in medical education in the age of artificial intelligence
Kim, K. (2025) · Forum for education studies. · doi:10.59400/fes2730
Titus, S., Meyer, R., & Hansen, A. (2025) · Medical Education · doi:10.1111/medu.15683
Inteligencia Artificial General en la educación en salud: ¿estamos realmente preparados?
Hernández, M. M. (2025) · INNOVACADEMIA · doi:10.29105/innoacad.v1i3.46
Shvarts, Y. G., Akimova, N. S., Евгеньевна, К. Л., Евгеньевна, Р. А., Parkhoniuk, E. V., & Kiselev, A. R. (2025) · Russian Open Medical Journal · doi:10.15275/rusomj.2025.0410
Skills Training in the Age of AI
Blakely, C., & Rose, R. L. (2026) · Springer eBooks · doi:10.1007/978-981-95-1622-3_5
Murry, L. T., Catledge, K., Travlos, D. V., & Engle, J. P. (2026) · Currents in Pharmacy Teaching and Learning · doi:10.1016/j.cptl.2026.102627
Sahin, C., & Çerçi, P. (2026) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2026.01.020
A qualitative study in evaluating an AI-driven patient simulation in interprofessional education
Dominguez, M., Lairamore, C., Casey, M., Pate, B., & Jeffrey, C. E. (2026) · Discover Education · doi:10.1007/s44217-026-01528-4
Seritan, A. L., Coverdale, J., Aggarwal, R., Morreale, M. K., Beresin, E. V., Balon, R., Guerrero, A. P. S., Castillo, E. G., Thomas, L. A., Louie, A. K., et al. (2026) · Academic Psychiatry · doi:10.1007/s40596-026-02367-z
Dominguez, M., & Lowe, L. M. (2026) · Bulletin of Faculty of Physical Therapy · doi:10.1186/s43161-026-00361-y
Yılmaz, H. Ö., Duman, E., & Şahin, K. (2026) · Journal of Nutrition · doi:10.1016/j.tjnut.2026.101739
The Future of Conscious Consumption I
Stiefenhofer, P. (2026) · Springer eBooks · doi:10.1007/978-3-032-22336-4_10
Niño, D. F., Masters, K., Patel, R. M., Bahous, S. A., Cheng, D., & Rossi, A. (2026) · Medical Teacher · doi:10.1080/0142159x.2026.2718319
Self-citationSiddiqi, W., AlFarouki, J., Krage, R., Mousa, H., Rivas, H., Michiels, B., & Hadjiat, Y. (2026) · doi:10.2196/preprints.113831
- Authors
- Masters, K., Herrmann-Werner, A., Festl-Wietek, T., & Taylor, D.
- Affiliations
- Sultan Qaboos University (OM)
- Tübingen Institute for Medical Education, University of Tübingen
- Gulf Medical University (AE)
- Corresponding author
- Masters, Ken
- Venue
- Medical Teacher
- Publisher
- Informa UK Limited
- Published
- 2024-08-08
- Volume
- 46
- Issue
- 10
- Pages
- 1258-1271
- ISSN
- 0142-159X (ISSN-L)
- 1466-187X
- OpenAlex
- W4401420291
- Cited by
- 46
- References
- 38
- Citations by year
- 2026: 19 · 2025: 22 · 2024: 5
- FWCI
- 4.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Teacher (publishedVersion)
- PubMed (publishedVersion)
- 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 general intelligence, generative AI, health professions education, artificial intelligence in healthcare, personalized learning, student adaptability, AI impact on employment, educational quality
- MeSH terms
- Artificial Intelligence, Health Occupations, Humans
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