Advantages and pitfalls in utilizing artificial intelligence for crafting medical examinations: a medical education pilot study with GPT-4
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Abstract
Background: The task of writing multiple choice question examinations for medical students is complex, timely and requires significant efforts from clinical staff and faculty. Applying artificial intelligence algorithms in this field of medical education may be advisable. Methods: During March to April 2023, we utilized GPT-4, an OpenAI application, to write a 210 multi choice questions-MCQs examination based on an existing exam template and thoroughly investigated the output by specialist physicians who were blinded to the source of the questions. Algorithm mistakes and inaccuracies, as identified by specialists were classified as stemming from age, gender or geographical insensitivities. Results: After inputting a detailed prompt, GPT-4 produced the test rapidly and effectively. Only 1 question (0.5%) was defined as false; 15% of questions necessitated revisions. Errors in the AI-generated questions included: the use of outdated or inaccurate terminology, age-sensitive inaccuracies, gender-sensitive inaccuracies, and geographically sensitive inaccuracies. Questions that were disqualified due to flawed methodology basis included elimination-based questions and questions that did not include elements of integrating knowledge with clinical reasoning. Conclusion: GPT-4 can be used as an adjunctive tool in creating multi-choice question medical examinations yet rigorous inspection by specialist physicians remains pivotal.
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Three studies reported the use of AI, particularly ChatGPT, for generating exam questions.
“Three studies reported the use of AI, particularly ChatGPT, for generating exam questions [133, 134, 136].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [134]
AI aided teachers in creating tests such as MCQs.
“aided teachers in creating tests such as MCQs [134, 135]”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [134]
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Additional file 1 of Large language models for generating medical examinations: systematic review
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Additional file 3 of Large language models for generating medical examinations: systematic review
Artsi, Y., Sorin, V., Konen, E., Glicksberg, B. S., Nadkarni, G., & Klang, E. (2024) · Figshare · doi:10.6084/m9.figshare.26699645
Additional file 2 of Large language models for generating medical examinations: systematic review
Artsi, Y., Sorin, V., Konen, E., Glicksberg, B. S., Nadkarni, G., & Klang, E. (2024) · Figshare · doi:10.6084/m9.figshare.26699642
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How to Use Large Language Model Chatbots in Multiple-choice Question Generation
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The Role of Artificial Intelligence in Medical Education and Training
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The Use of ChatGPT to Write Assessment Questions
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- Authors
- E, K., S, P., R, G., R, K. L., A, B., M, G., T, O., S, R., V, R., H, M., & G, S.
- Affiliations
- Tel Aviv University (IL)
- Venue
- BMC Medical Education
- Published
- 2023-10-17
- Volume
- 23
- Issue
- 1
- Pages
- 772-772
- ISSN
- 1472-6920 (ISSN-L)
- PMC
- PMC10580534
- OpenAlex
- W4387703131
- Cited by
- 89
- References
- 16
- Citations by year
- 2026: 20 · 2025: 42 · 2024: 25 · 2023: 2
- FWCI
- 3.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Education (publishedVersion, cc-by)
- Research Square (acceptedVersion, 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
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- GPT-4, large language models, artificial intelligence in medical education, multiple-choice question generation, clinical reasoning assessment
- MeSH terms
- Artificial Intelligence, Education, Medical, Educational Measurement, Humans, Pilot Projects, Writing
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 ›
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