ArticleKorean journal of radiology2026
Evaluating the Accuracy and Diagnostic Reasoning of Multimodal Large Language Models in Interpreting Neuroradiology Cases From
Article in Korean journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Effect of Fitzpatrick Skin Type Prompting on Diagnostic Accuracy in Multimodal Large Language Models: A Within-Image Experimental Study.Bioengineering (Basel, Switzerland) · 2026Article
- Benchmarking Multimodal Large Language Models for Cardiopulmonary Findings on Chest Radiographs: Sex-Stratified Discrimination and Operating Characteristics.Diagnostics (Basel, Switzerland) · 2026Article
- Evaluating Multimodal Large Language Models in Neuroradiology: Methodological Considerations.Korean journal of radiology · 2026Article
- Response to "Evaluating Multimodal Large Language Models in Neuroradiology: Methodological Considerations".Korean journal of radiology · 2026Article
Corrections and comments
- Commented on by
- Erratum issued
Authors and funding
7 authors.
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Abstract
objectiveTo evaluate the accuracy and reasoning capabilities of large multimodal language models compared with those of neuroradiology subspecialty-trained radiologists in neuroradiology case interpretation. MATERIALS AND
methodsThis experimental study used custom-made 401 radiologic quizzes derived from articles published in
resultsTop-3 accuracy (i.e., correct answers present among top-3 differential diagnoses) of LLMs ranged from 29.9% (120 of 401) to 49.4% (198 of 401, obtained with GPT-4V in the T1 setting), while radiologists achieved 80.3% (322 of 401) and 68.3% (274 of 401), respectively (
conclusionLLMs remarkably underperformed compared with neuroradiologists and showed unsatisfactory reasoning for their differential diagnoses, with performance declining further in cases without textual input of clinical history. These findings highlight the limitations of current multimodal LLMs in neuroradiological interpretation and their reliance on text input.
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