Evaluating large language models in medical applications: a survey
It addresses the critical need for reliable evaluation in healthcare, where LLMs have transformative potential, but the survey is incremental as it synthesizes existing studies without introducing new methods or results.
This survey tackles the problem of evaluating large language models (LLMs) in medical applications by providing a comprehensive overview of evaluation data sources, task scenarios, and methods, while identifying key challenges and opportunities for responsible integration into clinical practice.
Large language models (LLMs) have emerged as powerful tools with transformative potential across numerous domains, including healthcare and medicine. In the medical domain, LLMs hold promise for tasks ranging from clinical decision support to patient education. However, evaluating the performance of LLMs in medical contexts presents unique challenges due to the complex and critical nature of medical information. This paper provides a comprehensive overview of the landscape of medical LLM evaluation, synthesizing insights from existing studies and highlighting evaluation data sources, task scenarios, and evaluation methods. Additionally, it identifies key challenges and opportunities in medical LLM evaluation, emphasizing the need for continued research and innovation to ensure the responsible integration of LLMs into clinical practice.