Radiology-Llama2: Best-in-Class Large Language Model for Radiology
Zhengliang Liu Yiwei Li Peng Shu Aoxiao Zhong Longtao Yang Chao Ju Zihao Wu Chong Ma Jie Luo Cheng Chen Sekeun Kim Jiang Hu Haixing Dai Lin Zhao Dajiang Zhu Jun Liu Wei Liu Dinggang Shen Tianming Liu Quanzheng Li Xiang Li
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.