A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation
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arXiv
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| Natura: | Preprint |
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2024
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| _version_ | 1866916531596689408 |
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| author | Chen, Zhihong Varma, Maya Xu, Justin Paschali, Magdalini Van Veen, Dave Johnston, Andrew Youssef, Alaa Blankemeier, Louis Bluethgen, Christian Altmayer, Stephan Valanarasu, Jeya Maria Jose Muneer, Mohamed Siddig Eltayeb Reis, Eduardo Pontes Cohen, Joseph Paul Olsen, Cameron Abraham, Tanishq Mathew Tsai, Emily B. Beaulieu, Christopher F. Jitsev, Jenia Gatidis, Sergios Delbrouck, Jean-Benoit Chaudhari, Akshay S. Langlotz, Curtis P. |
| author_facet | Chen, Zhihong Varma, Maya Xu, Justin Paschali, Magdalini Van Veen, Dave Johnston, Andrew Youssef, Alaa Blankemeier, Louis Bluethgen, Christian Altmayer, Stephan Valanarasu, Jeya Maria Jose Muneer, Mohamed Siddig Eltayeb Reis, Eduardo Pontes Cohen, Joseph Paul Olsen, Cameron Abraham, Tanishq Mathew Tsai, Emily B. Beaulieu, Christopher F. Jitsev, Jenia Gatidis, Sergios Delbrouck, Jean-Benoit Chaudhari, Akshay S. Langlotz, Curtis P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12208 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation Chen, Zhihong Varma, Maya Xu, Justin Paschali, Magdalini Van Veen, Dave Johnston, Andrew Youssef, Alaa Blankemeier, Louis Bluethgen, Christian Altmayer, Stephan Valanarasu, Jeya Maria Jose Muneer, Mohamed Siddig Eltayeb Reis, Eduardo Pontes Cohen, Joseph Paul Olsen, Cameron Abraham, Tanishq Mathew Tsai, Emily B. Beaulieu, Christopher F. Jitsev, Jenia Gatidis, Sergios Delbrouck, Jean-Benoit Chaudhari, Akshay S. Langlotz, Curtis P. Computer Vision and Pattern Recognition Computation and Language 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. |
| title | A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2401.12208 |