Integrating large language models (LLMs) into neuro-oncologic radiology: a retrospective feasibility assessment of GPT-4o for brain tumor diagnosis.

purposeThis study aimed to retrospectively evaluate the diagnostic performance and reliability of GPT-4o, a Large Language Model (LLM), in interpreting radiological images, imaging findings, and patient clinical histories for brain tumor diagnosis within a pre-operative, pathology-confirmed patient cohort.

methodsWe retrospectively analyzed 239 pre-operative brain tumor cases diagnosed between December 2022 and September 2024. GPT-4o’s diagnostic accuracy was assessed under three predefined input methods: radiological images alone (Method 1), radiological findings combined with patient clinical histories (Method 2), and integration of both images and textual information (Method 3). Each case was evaluated independently three times using standardized, fixed prompts. Diagnostic performance was evaluated based on final and differential diagnosis accuracy rates for benign and malignant tumors.

resultsMethod 1 yielded relatively low final diagnostic accuracies (32% overall, 25% benign tumors) and moderate differential diagnostic accuracies (54% overall, 37% benign tumors). Method 2 demonstrated significantly higher accuracies, reaching 76% for final diagnosis (overall group) and 83% for differential diagnosis (overall group). Method 3 provided slight improvements compared to Method 2, although differences were not statistically significant.

conclusionIn this pre-operative brain tumor cohort, GPT-4o demonstrated higher diagnostic accuracy when provided with structured radiological findings and patient histories compared to image analysis alone. These results suggest the potential value of standardized textual inputs to enhance GPT-4o’s diagnostic performance. However, the clinical generalizability of these findings remains limited, and further prospective validation is needed before broader clinical adoption.