ArticleAdvances in medical education and practice2025
Application of Artificial Intelligence Generated Content in Medical Examinations.
Article in Advances in medical education and practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- AI-Generated Multiple Mini Interview (MMI) Stations for Medical School Admissions: Psychometric Evaluation.JMIR medical education · 2026Article
- Generative Artificial Intelligence-driven orthodontic education practices.BMC medical education · 2026Review
- Artificial Intelligence Generated Videos as Supportive Tools in Medical Education: A Scoping Review.Journal of medical education and curricular developmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the rapid development of large language model, artificial intelligence generated content (AIGC) presents novel opportunities for constructing medical examination questions. However, it is unclear about the way of effectively utilizing AIGC for designing medical questions. AIGC is characterized by its rapid response capabilities and high efficiency, as well as good performance in mimicking clinical realities. In this study, we revealed the limitations inherent in paper-based examinations, and provided a streamlined instruction for generating questions using AIGC, with a particular focus on multiple-choice questions, case study questions, and video questions. Manual review remains necessary to ensure the accuracy and quality of the generated content. Future development will be benefited from technologies like retrieval augmented generation, multi-agent system, and video generation technology. As AIGC continues to evolve, it is anticipated to bring transformative changes to medical examinations, enhancing the quality of examination preparation, and contributing to the effective cultivation of medical students.
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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.