An Overview of Medical Knowledge Evaluation of Large Language Models: An Endeavor Toward a Standardized Evaluation and Reporting Guideline

Abstract

Large language models (LLMs) have increasingly been recognized for their potential to revolutionize various aspects of healthcare, including diagnosis and treatment planning. However, the complexity of evaluating these models, particularly in the medical domain, has led to a lack of standardization in assessment methodologies. This study, conducted by the Farzan Clinical Research Institute, aims to establish a standardized evaluation framework for medical LLMs by proposing specific checklists for multiple-choice questions (MCQs), question-answering tasks, and case scenarios. The study demonstrates that MCQs provide a straightforward means to assess model accuracy, while the proposed confusion matrix helps identify potential biases in model choice. For question-answering tasks, the study emphasizes the importance of evaluating dimensions like relevancy, similarity, coherence, fluency, and factuality, ensuring that LLM responses meet clinical expectations. In case scenarios, the dual focus on accuracy and reasoning allows for a nuanced understanding of LLMs' diagnostic processes. The study also highlights the importance of model coverage, reproducibility, and the need for tailored evaluation methods to match study characteristics. The proposed checklists and methodologies aim to facilitate consistent and reliable assessments of LLM performance in medical tasks, paving the way for their integration into clinical practice. Future research should refine these methods and explore their application in real-world settings to enhance the utility of LLMs in medicine.

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