ArticleAcademic pathology
Evaluating artificial intelligence-generated multiple-choice questions in clinical pathology.
Article in Academic pathology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
6 authors.
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Abstract
Large language models are increasingly applied in medical education, but their role in clinical pathology remains uncertain. We conducted a prospective evaluation during the 2024-2025 academic year using GPT-4 with a standardized prompting protocol to generate multiple-choice questions for pathology resident education in clinical chemistry, hematopathology, clinical microbiology, molecular pathology, bioinformatics, and transfusion medicine. Faculty experts scored items for clarity and accuracy on a 1-5 scale and assigned a disposition of kept as is, kept but edited, or discarded. Of 260 items reviewed, 108 (41.5%) were retained without revision, 86 (33.1%) with edits, and 66 (25.4%) were discarded. Subspecialty performance varied, with molecular pathology showing the highest retention (60% unedited), while clinical microbiology had a relatively high discard rate (41.7%). Items retained without revision demonstrated significantly higher clarity and accuracy scores than discarded items (
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