Radiology report generation, as a key step in medical image analysis, is
critical to the quantitative analysis of clinically informed decision-making
levels. However, complex and diverse radiology reports with cross-source
heterogeneity pose a huge generalizability challenge to the current methods
under massive data volume, mainly because the style and normativity of
radiology reports are obviously distinctive among institutions, body regions
inspected and radiologists. Recently, the advent of large language models (LLM)
offers great potential for recognizing signs of health conditions. To resolve
the above problem, we collaborate with the Second Xiangya Hospital in China and
propose ChatRadio-Valuer based on the LLM, a tailored model for automatic
radiology report generation that learns generalizable representations and
provides a basis pattern for model adaptation in sophisticated analysts' cases.
Specifically, ChatRadio-Valuer is trained based on the radiology reports from a
single institution by means of supervised fine-tuning, and then adapted to
disease diagnosis tasks for human multi-system evaluation (i.e., chest,
abdomen, muscle-skeleton, head, and maxillofacial $\&$ neck) from six different
institutions in clinical-level events. The clinical dataset utilized in this
study encompasses a remarkable total of \textbf{332,673} observations. From the
comprehensive results on engineering indicators, clinical efficacy and
deployment cost metrics, it can be shown that ChatRadio-Valuer consistently
outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and
GPT-4 et al., in terms of the diseases diagnosis from radiology reports.
ChatRadio-Valuer provides an effective avenue to boost model generalization
performance and alleviate the annotation workload of experts to enable the
promotion of clinical AI applications in radiology reports.

ChatRadio-Valuer 是基于大型语言模型的自动生成放射学报告的模型，通过有监督的微调、模型自适应以及临床级事件的多系统评估，它在疾病诊断方面相比 ChatGPT 和 GPT-4 等现有模型表现更佳，提供了一种有效的提升模型泛化性能和减轻专家注释工作量的方式，从而推动放射学报告的临床 AI 应用的推广。