Assessing Diagnostic Precision and Therapeutic Guidance Using Artificial Intelligence in Functional Neurosurgery Cases

Abstract

Background and objective The integration of artificial intelligence (AI) into functional neurosurgery holds great promise for improving diagnostic precision and therapeutic decision-making. This study aimed to assess the diagnostic accuracy and treatment recommendations provided by five AI models - ChatGPT-3.5, ChatGPT-4, Perplexity, Gemini, and AtlasGPT - when applied to complex clinical cases. Methods Ten clinical cases related to functional neurosurgery were selected from the medical literature to minimize ambiguity and ensure clarity. Each case was presented to the AI models with the directive to propose a diagnosis and therapeutic approach, using medical terminology. The AI responses were evaluated by a panel of seven functional neurosurgeons, who scored the accuracy of diagnoses and treatment recommendations on a scale from 0 to 10. The scores were analyzed using one-way ANOVA, with post-hoc analysis via Tukey's test to identify significant differences among the AI models. Results Diagnostic accuracy varied significantly among the AI models. AtlasGPT achieved a median diagnostic score of 9 [quartile 1 (Q1): 9, quartile 3 (Q3): 10, interquartile range (IQR): 1], demonstrating superior performance compared to Perplexity, which had a median score of 9 with a higher IQR of 3 (p=0.04), and ChatGPT-3.5, which had a median score of 10 but with a lower IQR of 2 (p=0.03). In terms of treatment recommendations, AtlasGPT's median score was 8, notably higher than ChatGPT-3.5, which had a median score of 7 (p<0.01), and Perplexity, which also had a median score of 8 (p<0.01). Conclusions This study's findings underscore the potential of AI models in functional neurosurgery, particularly in enhancing diagnostic accuracy and expanding therapeutic options. However, the variability in performance among different AI systems suggests the need for continuous evaluation and refinement of these technologies. Rigorous assessment and interdisciplinary collaboration are essential to ensure the safe and effective integration of AI into clinical practice.

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