Evaluating large language models in medical applications: a survey

• URL: http://arxiv.org/abs/2405.07468v1
• Date: Mon, 13 May 2024 05:08:33 GMT
• Title: Evaluating large language models in medical applications: a survey
• Authors: Xiaolan Chen, Jiayang Xiang, Shanfu Lu, Yexin Liu, Mingguang He, Danli Shi,
• Abstract summary: Large language models (LLMs) have emerged as powerful tools with transformative potential across numerous domains. evaluating the performance of LLMs in medical contexts presents unique challenges due to the complex and critical nature of medical information.
• Score: 1.5923327069574245
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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