A Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industry
Yining Huang, Keke Tang, Meilian Chen, Boyuan Wang
TL;DR
This survey addresses the challenge of evaluating Large Language Models in the medical domain by detailing their evolving capabilities, deployment scenarios, and the specific evaluation frameworks required for healthcare. It provides a structured taxonomy of applications (clinical, data processing, research, education, and public health), a comprehensive review of evaluation methods (models, evaluators, metrics), and benchmarks/datasets across QA, summarization, information extraction, bioinformatics, and information retrieval. The paper highlights technical, ethical, and legal challenges, and proposes holistic strategies to advance multi-dimensional, evidence-based evaluation that supports safe, effective, and equitable clinical use. By synthesizing current evidence and outlining gaps, the work aims to guide practitioners, researchers, and policymakers in implementing robust validation protocols for AI-driven medical tools.
Abstract
Since the inception of the Transformer architecture in 2017, Large Language Models (LLMs) such as GPT and BERT have evolved significantly, impacting various industries with their advanced capabilities in language understanding and generation. These models have shown potential to transform the medical field, highlighting the necessity for specialized evaluation frameworks to ensure their effective and ethical deployment. This comprehensive survey delineates the extensive application and requisite evaluation of LLMs within healthcare, emphasizing the critical need for empirical validation to fully exploit their capabilities in enhancing healthcare outcomes. Our survey is structured to provide an in-depth analysis of LLM applications across clinical settings, medical text data processing, research, education, and public health awareness. We begin by exploring the roles of LLMs in various medical applications, detailing their evaluation based on performance in tasks such as clinical diagnosis, medical text data processing, information retrieval, data analysis, and educational content generation. The subsequent sections offer a comprehensive discussion on the evaluation methods and metrics employed, including models, evaluators, and comparative experiments. We further examine the benchmarks and datasets utilized in these evaluations, providing a categorized description of benchmarks for tasks like question answering, summarization, information extraction, bioinformatics, information retrieval and general comprehensive benchmarks. This structure ensures a thorough understanding of how LLMs are assessed for their effectiveness, accuracy, usability, and ethical alignment in the medical domain. ...