Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark

作者：Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David A. Clifton · 发表于：medRxiv · 年份：2024 · DOI：10.1101/2024.04.24.24306315 · 被引用次数：15 · 研究领域：Machine Learning in Healthcare、Biomedical Text Mining and Ontologies、Artificial Intelligence in Healthcare and Education

Abstract The adoption of large language models (LLMs) to assist clinicians has attracted remarkable attention. Existing works mainly adopt the closeended question-answering (QA) task with answer options for evaluation. However, many clinical decisions involve answering open-ended questions without pre-set options. To better understand LLMs in the clinic, we construct a benchmark ClinicBench . We first collect eleven existing datasets covering diverse clinical language generation, understanding, and reasoning tasks. Furthermore, we construct six novel datasets and clinical tasks that are complex but common in real-world practice, e.g., open-ended decision-making, long document processing, and emerging drug analysis. We conduct an extensive evaluation of twenty-two LLMs under both zero-shot and few-shot settings. Finally, we invite medical experts to evaluate the clinical usefulness of LLMs 1 .