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This research assesses the diagnostic accuracy of vision language models in neuroradiological image interpretation.

November 17, 2025npj Digital MedicineOpen Access

Evaluating the diagnostic accuracy of vision language models for neuroradiological image interpretation

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This research assesses the diagnostic accuracy of vision language models in neuroradiological image interpretation.
Comparative analysis of five vision language models with expert neuroradiologists
Used a dataset of 100 brain and spine cases from Radiopaedia
Measured accuracy based on differential diagnosis generation
Neuroradiologists achieved a mean accuracy of 86.2%
The best-performing VLM (Gemini 2.0) reached only 35% accuracy
High rates of diagnostic risks identified, including treatment delays in up to 45% of cases

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Implication

Evaluation reveals that vision-language models show lower diagnostic accuracy in imaging compared to expert neuroradiologists, indicating potential risks.

Cite This Study

Meddeb et al. (2025) studied this question.

synapsesocial.com/papers/692509e8c0ce034ddc352652 https://doi.org/10.1038/s41746-025-02047-6

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Implication

Evaluation reveals that vision-language models show lower diagnostic accuracy in imaging compared to expert neuroradiologists, indicating potential risks.

Evaluating the diagnostic accuracy of vision language models for neuroradiological image interpretation

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Discussion

Implication

Cite This Study

Evaluating the diagnostic accuracy of vision language models for neuroradiological image interpretation

Key Points

Discussion

Implication

Cite This Study

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