ArticlePLOS digital health2025
Evaluating ChatGPT-4 in the development of family medicine residency examinations.
Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Creating high-quality medical examinations is challenging due to time, cost, and training requirements. This study evaluates the use of ChatGPT 4.0 (ChatGPT-4) in generating medical exam questions for postgraduate family medicine (FM) trainees. Develop a standardized method for postgraduate multiple-choice medical exam question creation using ChatGPT-4 and compare the effectiveness of large language model (LLM) generated questions to those created by human experts. Eight academic FM physicians rated multiple-choice questions (MCQs) generated by humans and ChatGPT-4 across four categories: 1) human-generated, 2) ChatGPT-4 cloned, 3) ChatGPT-4 novel, and 4) ChatGPT-4 generated questions edited by a human expert. Raters scored each question on 17 quality domains. Quality scores were compared using linear mixed effect models. ChatGPT-4 and human-generated questions were rated as high quality, addressing higher-order thinking. Human-generated questions were less likely to be perceived as artificial intelligence (AI) generated, compared to ChatGPT-4 generated questions. For several quality domains ChatGPT-4 was non-inferior (at a 10% margin), but not superior, to human-generated questions. ChatGPT-4 can create medical exam questions that are high quality, and with respect to certain quality domains, non-inferior to those developed by human experts. LLMs can assist in generating and appraising educational content, leading to potential cost and time savings.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.