放射学报告生成中临床医生与专科基础模型之间的共识、分歧与协同
Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation
AI总结：
本研究构建了基于视觉-语言基础模型的胸部X光报告生成系统Flamingo-CXR，通过16名放射科医生评估验证其报告质量，并首次展示临床医生与AI协作撰写报告，在多数病例中达到或优于专家单独撰写的效果。
摘要：
放射学报告是现代医学中不可或缺的组成部分，为诊断和治疗等关键临床决策提供信息。然而，全球范围内放射科医生的短缺限制了患者获得专家诊疗的机会，并造成沉重的工作负担，导致可避免的错误和报告交付延迟。尽管近期基于视觉-语言模型的自动报告生成技术进展显示出改善这一状况的明显潜力，但通往实际应用的道路一直受到评估AI生成报告临床质量这一挑战的阻碍。在本研究中，我们构建了一个最先进的胸部X光片报告生成系统——Flamingo-CXR，通过对一个知名的视觉-语言基础模型在放射学数据上进行微调而实现。为了评估AI生成报告的质量，一组16名认证放射科医生对来自美国重症监护环境和印度住院环境的胸部X光片的AI生成报告和人工撰写报告进行了详细评估。在两个数据集中，超过60%的病例中，至少有一名放射科医生（每例两名中的一名）更偏好AI报告而非真实报告。在包含错误的AI生成报告子集中，最常被提及的原因与位置和发现相关，而人工撰写的报告中，大多数错误与严重程度和发现相关。这种差异表明我们的AI系统与人类专家之间可能存在互补性，促使我们开发了一个辅助场景，其中Flamingo-CXR先生成初稿报告，随后由临床医生进行修订。这是临床医生与AI协作撰写报告的首次展示，最终生成的报告在80%的住院病例和60%的重症监护病例中，被至少一名放射科医生评估为与仅由专家撰写的报告相当或更受青睐。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
