  The rise of large language models (LLMs) has marked a pivotal shift in the
field of natural language processing (NLP). LLMs have revolutionized a
multitude of domains, and they have made a significant impact in the medical
field. Large language models are now more abundant than ever, and many of these
models 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 radiology
NLP. This study seeks to bridge this gap by critically evaluating thirty two
LLMs in interpreting radiology reports, a crucial component of radiology NLP.
Specifically, the ability to derive impressions from radiologic findings is
assessed. The outcomes of this evaluation provide key insights into the
performance, strengths, and weaknesses of these LLMs, informing their practical
applications within the medical domain.

Computation and Language