ChatGPT-Based Learning: Generative Artificial Intelligence in Medical Education
Open accessAbstract
Large language models like ChatGPT are a type of machine learning model that can offer a positive paradigm shift in case-based/problem-based learning (CBL/PBL). ChatGPT may be able to augment the existing paradigm to work in conjunction with the clinical-teacher in PBL/CBL case generation. It can develop realistic patient cases that could be revised by clinical teachers to ensure accuracy and relevance. Further, it can be directed to include specific case content in order to facilitate the constructive alignment of the case with the broader learning objectives of the curriculum. There is also the possibility of improving engagement by 'gamifying' CBL/PBL.Supplementary information The online version contains supplementary material available at 10.1007/s40670-023-01934-5.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Most existing research on GPT-based systems in medical education remains conceptual or exploratory, with limited empirical evidence on practical implementation in specific skill domains.
“However, most existing research remains conceptual or exploratory, with limited empirical evidence on the practical implementation of GPT-based systems in specific skill domains.”
From Feasibility study of using GPT for history-taking training in medical education: a randomized clinical trial (2025) · cites this paper as [4]
The paper’s conclusion (page unavailable in our copy) calls for further research on the effectiveness and implementation of ChatGPT-generated CBL/PBL cases, supporting a gap in practical evidence for that application. It does not establish that most existing research across GPT-based medical education is conceptual or exploratory, or assess evidence across specific skill domains; it also describes existing experimental work on medical proficiency. (LLM, conf=0.83)
Experimental — accuracy not yet measuredModels like GPT-3, GPT-4, Copilot, and LLaMA 3 have expanded GAI applications from basic automation to sophisticated tasks including content creation, data analysis, and intelligent question-answering systems.
“Models like GPT-3, GPT-4, Copilot, and LLaMA 3 have expanded GAI applications from basic automation to sophisticated tasks including content creation, data analysis, and intelligent question-answering systems [3].”
From Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review (2025) · cites this paper as [3]
The paper’s Abstract says large language models generate responses to questions, and describes ChatGPT creating educational cases and contributing to medical case reports. It does not substantiate the claimed progression from basic automation or applications in data analysis, nor does it discuss the named set of GPT-3, GPT-4, Copilot, and LLaMA 3. (LLM, conf=0.87)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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
Dave, T., Athaluri, S. A., & Singh, S. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1169595 · Cited by 1,204 · see citing works
Challenges and opportunities facing medical education.
Densen, P. (2011) · PubMed Central · Cited by 484 · see citing works
Hirosawa, T., Harada, Y., Yokose, M., Sakamoto, T., Kawamura, R., & Shimizu, T. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20043378 · Cited by 344 · see citing works
The Advent of Generative Language Models in Medical Education
Karabacak, M., Ozkara, B. B., Margetis, K., Wintermark, M., & Bisdas, S. (2023) · JMIR Medical Education · doi:10.2196/48163 · Cited by 208 · see citing works
Sandoval, Y., Apple, F. S., Mahler, S. A., Body, R., Collinson, P. O., Jaffe, A. S., & on behalf of the International Federation of Clinical Chemistry and Laboratory Medicine Committee on the Clinical Application of Cardiac Biomarkers (2022) · Circulation · doi:10.1161/circulationaha.122.059678 · Cited by 202 · see citing works
Assessment of educational games for health professions: A systematic review of trends and outcomes
Abdulmajed, H., Park, Y. S., & Tekian, A. S. (2015) · Medical Teacher · doi:10.3109/0142159x.2015.1006609 · Cited by 117 · see citing works
Busch, F., Adams, L. C., & Bressem, K. K. (2023) · Medical Science Educator · doi:10.1007/s40670-023-01815-x · Cited by 88 · see citing works
Twelve tips for constructing problem-based learning cases
Azer, S. A., Peterson, R., Guerrero, A. P. S., & Edgren, G. (2012) · Medical Teacher · doi:10.3109/0142159x.2011.613500 · Cited by 77 · see citing works
How will artificial intelligence change medical training?
Kundu, S. (2021) · Communications Medicine · doi:10.1038/s43856-021-00003-5 · Cited by 43 · see citing works
Clinical vignettes improve performance in anatomy practical assessment
Ikah, D. S. K., Finn, G. M., Swamy, M., White, P. M., & McLachlan, J. C. (2014) · Anatomical Sciences Education · doi:10.1002/ase.1471 · Cited by 29 · see citing works
Hanna, J. J., Wakene, A. D., Lehmann, C. U., & Medford, R. J. (2023) · doi:10.1101/2023.08.28.23294730 · Cited by 28 · see citing works
Nachshon, A., Batzofin, B., Beil, M., & van Heerden, P. V. (2023) · Cureus · doi:10.7759/cureus.35649 · Cited by 18 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Ikah DSK, Finn GM, Swamy M, White PM, McLachlan JC. Clini- cal vignettes improve performance in anatomy practical assess- ment. Anat Sci Educ. 2015;8(3):221-9.
Cited by
Sorted by citations
Large Language Model‐Based Chatbots in Higher Education
Yigci, D., Eryilmaz, M., Yetisen, A. K., Tasoglu, S., & Ozcan, A. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202400429 · Cited by 113 · see citing works
Wang, Z., Fan, T.-T., Li, M.-L., Zhu, N.-J., & Wang, X.-C. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07614-9 · Cited by 32 · see citing works
“Recent studies have increasingly explored the integration of large language models, particularly ChatGPT, into various domains of medical education [4, 5].”
Lin, Y., Luo, Z., Ye, Z., Zhong, N., Zhao, L., Zhang, L., Li, X., Chen, Z., & Chen, Y. (2025) · JMIR Medical Education · doi:10.2196/71125 · Cited by 31 · see citing works
“Models like GPT-3, GPT-4, Copilot, and LLaMA 3 have expanded GAI applications from basic automation to sophisticated tasks including content creation, data analysis, and intelligent question-answering systems [3].”
Fąferek, J., Kononowicz, A. A., Bogutska, N., Da Silva Domingues, V., Davydova, N., Frankowska, A., Iguacel, I., Mayer, A., Morin, L., Pavlyukovich, N., et al. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08006-9 · Cited by 10 · see citing works
- Authors
- Stretton, B., Kovoor, J., Arnold, M., & Bacchi, S.
- Affiliations
- The University of Adelaide (AU)
- Corresponding author
- Stretton, Brandon
- Venue
- Medical Science Educator
- Published
- 2023-11-08
- Volume
- 34
- Issue
- 1
- Pages
- 215-217
- ISSN
- 2156-8650 (ISSN-L)
- PMC
- PMC10948641
- OpenAlex
- W4388494284
- Cited by
- 34
- References
- 13
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 8
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Medical Science Educator (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- 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
- ChatGPT, generative AI, medical education, problem-based learning, case-based learning, case generation, clinical instructors, constructive alignment, gamification, student engagement
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
