DeepSeek vs ChatGPT: a comparison study of their performance in answering prostate cancer radiotherapy questions in multiple languages

Abstract

INTRODUCTION: The medical information generated by large language models (LLM) is crucial for improving patient education and clinical decision-making. This study aims to evaluate the performance of two LLMs (DeepSeek and ChatGPT) in answering questions related to prostate cancer radiotherapy in both Chinese and English environments. Through a comparative analysis, we aim to determine which model can provide higher-quality answers in different language environments. METHODS: A structured evaluation framework was developed using a set of clinically relevant questions covering three key domains: foundational knowledge, patient education, and treatment and follow-up care. Responses from DeepSeek and ChatGPT were generated in both English and Chinese and independently assessed by a panel of five oncology specialists using a five-point Likert scale. Statistical analyses, including the Wilcoxon signed-rank test, were performed to compare the models' performance across different linguistic contexts. RESULTS: = 0.125). These findings underscore DeepSeek's superior Chinese proficiency and language-specific optimization impacts. CONCLUSIONS: This study shows that DeepSeek performs excellently in providing Chinese medical information, while the two models perform similarly in an English environment. These findings underscore the importance of selecting language-specific artificial intelligence (AI) models to enhance the accuracy and reliability of medical AI applications. While both models show promise in supporting patient education and clinical decision-making, human expert review remains necessary to ensure response accuracy and minimize potential misinformation.

Ähnliche Arbeiten