Radiology reports are an instrumental part of modern medicine, informing key clinical decisions such as diagnosis and treatment. The worldwide shortage of radiologists, however, restricts access to expert care and imposes heavy workloads, contributing to avoidable errors and delays in report delivery. While recent progress in automated report generation with vision-language models offer clear potential in ameliorating the situation, the path to real-world adoption has been stymied by the challenge of evaluating the clinical quality of AI-generated reports. In this study, we build a state-of-the-art report generation system for chest radiographs, $\textit{Flamingo-CXR}$, by fine-tuning a well-known vision-language foundation model on radiology data. To evaluate the quality of the AI-generated reports, a group of 16 certified radiologists provide detailed evaluations of AI-generated and human written reports for chest X-rays from an intensive care setting in the United States and an inpatient setting in India. At least one radiologist (out of two per case) preferred the AI report to the ground truth report in over 60$\%$ of cases for both datasets. Amongst the subset of AI-generated reports that contain errors, the most frequently cited reasons were related to the location and finding, whereas for human written reports, most mistakes were related to severity and finding. This disparity suggested potential complementarity between our AI system and human experts, prompting us to develop an assistive scenario in which Flamingo-CXR generates a first-draft report, which is subsequently revised by a clinician. This is the first demonstration of clinician-AI collaboration for report writing, and the resultant reports are assessed to be equivalent or preferred by at least one radiologist to reports written by experts alone in 80$\%$ of in-patient cases and 60$\%$ of intensive care cases.

翻译：放射学报告是现代医学的重要组成部分，为诊断与治疗等关键临床决策提供依据。然而，全球放射科医生的短缺限制了专家诊疗的可及性，并导致工作负荷过重，进而引发报告交付中可避免的误差与延迟。尽管近期基于视觉-语言模型的自动报告生成技术展现了缓解现状的潜力，但实际应用仍受限于AI生成报告临床质量的评估难题。本研究通过微调通用视觉-语言基础模型构建了胸部X光片最新报告生成系统Flamingo-CXR。为评估AI生成报告的质量，16名持证放射科医生对来自美国重症监护病房与印度住院病房的胸部X光片，分别针对AI生成报告及人工编写报告进行了详细评估。结果显示，在两个数据集中，超过60%的病例中至少有一名放射科医生（每组两名）更倾向于AI报告而非真实报告。在含错误的AI生成报告子集中，最常被引述的错误原因与病灶位置及发现相关；而人工报告的错误多与严重程度及发现相关。这种差异表明AI系统与人类专家间存在潜在互补性，促使我们开发辅助场景：由Flamingo-CXR生成初稿报告，再由临床医生进行修订。这是首次对临床医生-AI协作撰写报告的验证，评估结果显示，在住院病例与重症监护病例中，分别有80%和60%的病例中至少有一名放射科医生认为协作生成的报告质量与专家独立编写的报告相当或更优。