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, 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 66$\%$ of intensive care cases.

翻译：放射学报告是现代医学的重要组成部分，为诊断和治疗等关键临床决策提供依据。然而，全球范围内放射科医生的短缺限制了患者获得专业诊疗的机会，并导致其工作负担过重，进而引发报告中可避免的错误和延迟。尽管近年来基于视觉-语言模型的自动报告生成技术取得进展，展现出改善这一状况的潜力，但评估AI生成报告的临床质量这一难题始终阻碍着其在实际场景中的应用。本研究通过将著名的视觉-语言基础模型在放射学数据上进行微调，构建了用于胸部X光片的最先进报告生成系统Flamingo-CXR。为评估AI生成报告的质量，16名持证放射科医生对AI生成报告和人工撰写报告进行了详细评估，这些报告分别来自美国的重症监护病房和印度的住院病房。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（每份病例由两名医生评估）认为AI报告优于真实报告。在含有错误的AI生成报告中，最常见的错误原因与病灶位置和发现描述相关；而人工撰写报告的错误则多集中于严重程度和发现描述。这一差异表明我们的AI系统与人类专家之间可能存在互补性，促使我们开发了一种辅助协作模式：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与AI在报告撰写中的协作进行论证。经评估，采用该协作模式生成的报告在住院病例中80%的情况下、在重症监护病例中66%的情况下，至少被一名放射科医生认为与专家单独撰写的报告质量相当或更优。