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Radiology report generation, as a key step in medical image analysis, iscritical to the quantitative analysis of clinically informed decision-makinglevels. However, complex and diverse radiology reports with cross-sourceheterogeneity pose a huge generalizability challenge to the current methodsunder massive data volume, mainly because the style and normativity ofradiology reports are obviously distinctive among institutions, body regionsinspected and radiologists. Recently, the advent of large language models (LLM)offers great potential for recognizing signs of health conditions. To resolvethe above problem, we collaborate with the Second Xiangya Hospital in China andpropose ChatRadio-Valuer based on the LLM, a tailored model for automaticradiology report generation that learns generalizable representations andprovides a basis pattern for model adaptation in sophisticated analysts' cases.Specifically, ChatRadio-Valuer is trained based on the radiology reports from asingle institution by means of supervised fine-tuning, and then adapted todisease diagnosis tasks for human multi-system evaluation (i.e., chest,abdomen, muscle-skeleton, head, and maxillofacial $\&$ neck) from six differentinstitutions in clinical-level events. The clinical dataset utilized in thisstudy encompasses a remarkable total of \textbf{332,673} observations. From thecomprehensive results on engineering indicators, clinical efficacy anddeployment cost metrics, it can be shown that ChatRadio-Valuer consistentlyoutperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) andGPT-4 et al., in terms of the diseases diagnosis from radiology reports.ChatRadio-Valuer provides an effective avenue to boost model generalizationperformance and alleviate the annotation workload of experts to enable thepromotion of clinical AI applications in radiology reports.

ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data