Comparative performance evaluation of large language models in answering esophageal cancer-related questions: a multi-model assessment study.

ABSTRACT:

Background

Esophageal cancer has high incidence and mortality rates, leading to increased public demand for accurate information. However, the reliability of online medical information is often questionable. This study systematically compared the accuracy, completeness, and comprehensibility of mainstream large language models (LLMs) in answering esophageal cancer-related questions.

Methods

In total, 65 questions covering fundamental knowledge, preoperative preparation, surgical treatment, and postoperative management were selected. Each model, namely, ChatGPT 5, Claude Sonnet 4.0, DeepSeek-R1, Gemini 2.5 Pro, and Grok-4, was queried independently using standardized prompts. Five senior clinical experts, including three thoracic surgeons, one radiologist, and one medical onc

SUBMITTER: He Z 

PROVIDER: S-EPMC12537878 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature