Evaluating and Mitigating Limitations of Large Language Models in Clinical Decision Making

Abstract

Abstract Clinical decision making is one of the most impactful parts of a physician’s responsibilities and stands to benefit greatly from AI solutions and large language models (LLMs) in particular. However, while LLMs have achieved excellent performance on medical licensing exams, these tests fail to assess many skills that are necessary for deployment in a realistic clinical decision making environment, including gathering information, adhering to established guidelines, and integrating into clinical workflows. To understand how useful LLMs are in real-world settings, we must evaluate them in the wild , i.e. on real-world data under realistic conditions. Here we have created a curated dataset based on the MIMIC-IV database spanning 2400 real patient cases and four common abdominal pathologies as well as a framework to simulate a realistic clinical setting. We show that current state-of-the-art LLMs do not accurately diagnose patients across all pathologies (performing significantly worse than physicians on average), follow neither diagnostic nor treatment guidelines, and cannot interpret laboratory results, thus posing a serious risk to the health of patients. Furthermore, we move beyond diagnostic accuracy and demonstrate that they cannot be easily integrated into existing workflows because they often fail to follow instructions and are sensitive to both the quantity and order of information. Overall, our analysis reveals that LLMs are currently not ready for clinical deployment while providing a dataset and framework to guide future studies.

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