Clinical feasibility of AI Doctors: Evaluating the replacement potential of large language models in outpatient settings for central nervous system tumors.

BACKGROUND AND OBJECTIVES

The treatment of central nervous system (CNS) tumors is complex and resource-intensive, with higher mortality in underserved regions. Large language models (LLMs) show promise in medical support, but their real-world performance in CNS tumor outpatient care remains unclear. This study aims to assess the diagnostic and treatment capabilities of LLMs in bilingual clinical settings.

METHODS

This retrospective study evaluated three LLMs (ChatGPT-4o, DeepSeek-R1, and Doubao) in assisting neuro-oncology outpatient decision-making within bilingual (Chinese/English) clinical environments. A total of 338 outpatient cases were included, with each model assigned three clinical tasks: differential diagnosis, main diagnosis, and treatment advice. Model outputs were compared against assessments by experienced neurosurgeons. Statistical analysis employed McNemar tests (P < 0.05).

RESULTS

ChatGPT-4o and DeepSeek-R1 achieved over 90 % accuracy in differential diagnosis, showing no significant difference compared to doctors (P > 0.05), while Doubao performed significantly worse (Chinese: P = 0.02, English: P = 0.01). In main diagnosis, both ChatGPT-4o and DeepSeek-R1 showed no significant deviation from doctors performance (P > 0.05), whereas Doubao underperformed (Chinese: P = 0.019, English: P = 0.011). For treatment recommendations, all models showed reduced accuracy (ChatGPT-4o: 80.5 %; DeepSeek-R1: 79 %; Doubao: 71.3 %), significantly lower than doctors (Whether in Chinese or English: P < 0.05). No performance difference was observed between Chinese and English cases.

CONCLUSION

LLMs show strong potential in the preliminary diagnosis and decision support for CNS tumors, and their cross-lingual adaptability underscores their clinical feasibility.