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This is a Experimental Study study.

October 22, 2025Open Access

Performance of Artificial Intelligence and Large Language Models (LLMs) on Neurosurgical Board Examinations Across Text and Visual Modalities

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Current artificial intelligence approaches show varied performance on neurosurgical board examinations, achieving accuracies of around 68%.
Latency for responses varied significantly among models, hitting extremes from 0.22 seconds to over 27 seconds.
Assessment covered 476 neurosurgical questions across 13 subspecialties, focusing reliably on visual and text modalities.
Improved accuracy with visual inputs was evident in certain subspecialties, yet issues with clinical history highlight need for better model alignment.

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Benchmarking AI accuracy on neurosurgical exams reveals insights into multimodal performance and limitations.

Kashlan et al. (2025) studied this question.

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