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The rise of large language models (LLMs) has marked a pivotal shift in thefield of natural language processing (NLP). LLMs have revolutionized amultitude of domains, and they have made a significant impact in the medicalfield. Large language models are now more abundant than ever, and many of thesemodels exhibit bilingual capabilities, proficient in both English and Chinese.However, a comprehensive evaluation of these models remains to be conducted.This lack of assessment is especially apparent within the context of radiologyNLP. This study seeks to bridge this gap by critically evaluating thirty twoLLMs in interpreting radiology reports, a crucial component of radiology NLP.Specifically, the ability to derive impressions from radiologic findings isassessed. The outcomes of this evaluation provide key insights into theperformance, strengths, and weaknesses of these LLMs, informing their practicalapplications within the medical domain.

Evaluating Large Language Models for Radiology Natural Language Processing