Aug 2026· Journal of imaging informatics in medicine· 1 citation· 32 references
Medicine
TL;DR
Multimodal large language models are best positioned as supervised tools requiring radiologist verification and item-level psychometric evaluation in multiple-choice questions generated by GPT-4o and o3 from correctly recognized radiographs and compare selected items with faculty-written questions.
This study provides a benchmark of LLM performance for radiology OSCE-style content generation and evaluation during a specific snapshot of artificial intelligence development (August 2024).
Ankush Ankush, Samriddhi Burman, Sydney Smith et al.· Radiology Advances· 0 citations
There may be significant differences in how effectively LLMs support patients with surgical queries, particularly in areas needing detailed explanation, and usually required minimal clarification in areas needing detailed explanation.
T. Davis, B. Guevel, K. Logishetty et al.· Annals of the Royal College...· 0 citations
Objectives: Large language models (LLMs) have emerged as promising tools for generating educational assessment items; however, their ability to produce multiple-choice questions (MCQs) that accurately reflect intended cognitive levels remains uncertain. This study evaluated the educational quality and cognitive alignme...
M. Medi̇şoğlu, Abdullah Örs, M. Öçbe· Anatomy· 0 citations
This data article describes a multimodal collection of scanned examination answers paired with expert-designed Outcome-Based Education (OBE) grading metadata. The collection contains 485 answer submissions from 415 consenting students at four academic institutions. Eight faculty contributors supplied examination materi...
SM JahangirAlam, Md. Khalid Syfullah, Saad Ahmed et al.· 1 citation
Evaluating the response accuracy of 2 advanced large language models found the Gemini-3.0 Flash responses excelled in highly structured, direct-response questions to relevance, clarity, and closeness to the article, whereas the ChatGPT-5.2 response demonstrated greater scientific accuracy at all levels and performed be...
Pranay Jain, F. R, A. V et al.· The Journal of prosthetic de...· 0 citations
Clinical vignettes were the primary contributor to improving LLM performance on OITE whereas images had minimal effect and closed-source models achieved substantially higher accuracy than open-source models, and all models performed above chance when provided answer options alone.
F. Gafoor, M. Syed, M. Halai et al.· medRxiv· 0 citations
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