This paper introduces Radiology-Llama2, a large language model specialized
for radiology through a process known as instruction tuning. Radiology-Llama2
is based on the Llama2 architecture and further trained on a large dataset of
radiology reports to generate coherent and clinically useful impressions from
radiological findings. Quantitative evaluations using ROUGE metrics on the
MIMIC-CXR and OpenI datasets demonstrate that Radiology-Llama2 achieves
state-of-the-art performance compared to other generative language models, with
a Rouge-1 score of 0.4834 on MIMIC-CXR and 0.4185 on OpenI. Additional
assessments by radiology experts highlight the model's strengths in
understandability, coherence, relevance, conciseness, and clinical utility. The
work illustrates the potential of localized language models designed and tuned
for specialized domains like radiology. When properly evaluated and deployed,
such models can transform fields like radiology by automating rote tasks and
enhancing human expertise.

Radiology-Llama2 是一个基于 Llama2 体系结构的大型语言模型，通过指令调整的过程在放射学领域达到了最先进的性能，它可以根据放射学检查结果生成连贯且临床有用的印象。