ArticleMedical education online2026
Agreement, calibration, and failure of three large language models as high-stakes multimodal ospe graders: a comparative psychometric analysis.
Article in Medical education online, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Clinical validation of a web-based decision support system for periodontal diagnosis, risk assessment, and recall planning: A multicenter study.Journal of oral biology and craniofacial researchArticle
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Authors and funding
11 authors.
Funding
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Abstract
backgroundMedical education faces increasing demand for scalable grading solutions. Large language models have been proposed as automated graders for high-stakes assessments, but evidence for their reliability in multimodal practical examinations remains limited.
methodsThis retrospective inter-rater reliability study compared three LLMs: ChatGPT-4o, Gemini 2.5 Flash, and Claude 3.5 Haiku with original human reference scores on 19 integrated anatomy, histology, and physiology objective structured practical examination items completed by 309 pre-medical students. Human reference scores were assigned during live summative grading by two content experts who divided items, with each item marked by one expert across all students; no human inter-rater reliability estimate was available. Items required simultaneous image interpretation and short-answer responses. Models were evaluated using zero-shot prompting reflecting deployment-realistic conditions. Agreement was assessed using Spearman's
resultsRank-order correlations were strong across all models (
conclusionsStrong rank-order correlation alone does not support LLM grading for categorical decisions in high-stakes assessment contexts. LLMs may serve as assessment assistants for first-pass ranking and discrepancy flagging, but item-level human review of flagged discordances, error-profile monitoring, and preserved human authority over pass/fail decisions are required before high-stakes deployment.
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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.