ArticleProceedings (Baylor University. Medical Center)2025
Can ChatGPT generate surgical multiple-choice questions comparable to those written by a surgeon?
Article in Proceedings (Baylor University. Medical Center), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Automated Generation and Human Evaluation of Neurosurgical Board Examination Self-Assessment Questions.Neurosurgery practice · 2026Article
- A scoping review of the use of generative artificial intelligence tools in health profession education.BMC medical education · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aimed to determine whether surgical multiple-choice questions generated by ChatGPT are comparable to those written by human experts (surgeons). Methods: The study was conducted at a medical school and involved 112 fourth-year medical students. Based on five learning objectives in general surgery (colorectal, gastric, trauma, breast, thyroid), ChatGPT and surgeons generated five multiple-choice questions. No change was made to the ChatGPT-generated questions. The statistical properties of these questions, including correlations between two group of questions and correlations with total scores (item discrimination) in a general surgery clerkship exam, were reported. Results: There was a significant positive correlation between the ChatGPT-generated and human-written questions for one learning objective (colorectal). More importantly, only one ChatGPT-generated question (colorectal) achieved an acceptable discrimination level, while other four failed to achieve it. In contrast, human-written questions showed acceptable discrimination levels. Conclusion: While ChatGPT has the potential to generate multiple-choice questions comparable to human-written ones in specific contexts, the variability across surgical topics points to the need for human oversight and review before their use in exams. It is important to integrate artificial intelligence tools like ChatGPT with human expertise to enhance efficiency and quality.
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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.