A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation

作者：Zhihong Chen, Maya Varma, Xu, Justin, Magdalini Paschali, Dave Van Veen, Andrew Johnston, Alaa Youssef, Louis Blankemeier, Bluethgen, Christian, Altmayer, Stephan, Jeya Maria Jose Valanarasu, Muneer, Mohamed Siddig Eltayeb, Eduardo Pontes Reis, Joseph Cohen, Cameron Olsen, Tanishq Mathew Abraham, Emily B. Tsai, Beaulieu, Christopher F., Jitsev, Jenia, Sergios Gatidis, Jean-Benoit Delbrouck, Akshay Chaudhari, Curtis P. Langlotz · 发表于：arXiv (Cornell University) · 年份：2024 · DOI：10.48550/arxiv.2401.12208 · 被引用次数：26 · 研究领域：COVID-19 diagnosis using AI、Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment

Over 1.4 billion chest X-rays (CXRs) are performed annually due to their cost-effectiveness as an initial diagnostic test. This scale of radiological studies provides a significant opportunity to streamline CXR interpretation and documentation. While foundation models are a promising solution, the lack of publicly available large-scale datasets and benchmarks inhibits their iterative development and real-world evaluation. To overcome these challenges, we constructed a large-scale dataset (CheXinstruct), which we utilized to train a vision-language foundation model (CheXagent). We systematically demonstrated competitive performance across eight distinct task types on our novel evaluation benchmark (CheXbench). Beyond technical validation, we assessed the real-world utility of CheXagent in directly drafting radiology reports. Our clinical assessment with eight radiologists revealed a 36% time saving for residents using CheXagent-drafted reports, while attending radiologists showed no significant time difference editing resident-drafted or CheXagent-drafted reports. The CheXagent-drafted reports improved the writing efficiency of both radiology residents and attending radiologists in 81% and 61% of cases, respectively, without loss of quality. Overall, we demonstrate that CheXagent can effectively perform a variety of CXR interpretation tasks and holds potential to assist radiologists in routine clinical workflows.