ArticleBMC research notes2026
Evaluating the ability of AI models to generate level-specific medical MCQs with variable difficulty.
Article in BMC research notes, 2026. 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
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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
1 citing paper in PubMed.
- Generating Image-Based Multiple-Choice Questions with Multimodal Large Language Models: Expert and Psychometric Evaluation.Journal of imaging informatics in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveArtificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT, is increasingly applied in medical education to automate assessment design. However, concerns persist regarding the content accuracy, cognitive depth, and psychometric validity of AI-generated multiple-choice questions (MCQs).
resultsA mixed-methods study was conducted to evaluate a structured prompt guiding ChatGPT in generating clinically relevant, single-best-answer MCQs in pediatrics. The prompt defined item count, subdomain distribution, difficulty, and examinee level. Following seven iterative refinements, ChatGPT produced 100 MCQs targeting clinical-year medical students. Two blinded experts independently assessed each item for clarity, content accuracy, clinical realism, distractor plausibility, and cognitive alignment. All items were structurally coherent and linguistically sound. Content accuracy was rated high in 87% of items, and stem clarity in 82%. Distractor plausibility was acceptable in 77%, though 23% of items contained at least one implausible distractor. Agreement between AI-predicted and expert-rated difficulty was low (κ = 0.06), suggesting limited calibration.
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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.