Human‐in‐the‐loop: Human involvement in enhancing medical inquiry performance in large language models.

This article discusses the role of human involvement in enhancing the performance of large language models (LLMs) in medical inquiry. The authors highlight the occasional shortcomings of LLMs in providing accurate citation information and accessing real-time data. They recommend prompt engineering as a way to enhance model performance, which involves carefully crafting instructions or queries given to LLMs to elicit specific and desired responses. The article also discusses the importance of verifying LLM outputs and acknowledges the limitations of LLMs in medical diagnoses and personalized advice. The authors conclude that the judicious implementation of the "human-in-the-loop" strategy, with a focus on prompt engineering, can greatly improve LLM capabilities in medical inquiry. [Extracted from the article]