Intra-axial primary brain tumor differentiation: comparing large language models on structured MRI reports vs. radiologists on images

Abstract

Question How do Large Language Models (LLM) perform when differentiating complex intra-axial primary brain tumors from structured MRI reports compared to radiologists interpreting images? Findings Radiologists outperformed all tested LLMs in diagnostic accuracy. The best model, GPT-4, showed promise but lagged considerably behind radiologists, particularly for less common tumors. Clinical relevance LLMs show potential as assistive tools for generating differential diagnoses from structured MRI reports, particularly for non-specialists, but they cannot currently replace the nuanced diagnostic expertise of a board-certified radiologist interpreting the primary image data.

Ähnliche Arbeiten