Peer-Reviewed Publication
Patterns (N Y)2023;4(9):100802.September 8, 2023Journal Article
Evaluating progress in automatic chest X-ray radiology report generation.
Feiyang Yu1, Mark Endo1, Rayan Krishnan1, Ian Pan2, Andy Tsai3, Eduardo Pontes Reis4, Eduardo Kaiser Ururahy Nunes Fonseca4, Henrique Min Ho Lee4, Zahra Shakeri Hossein Abad5, Andrew Y Ng1, Curtis P Langlotz6, Vasantha Kumar Venugopal7, Pranav Rajpurkar8
1Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
2Department of Radiology, Brigham and Women's Hospital, Boston, MA 02115, USA.
3Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
4Cardiothoracic Radiology Group, Hospital Israelita Albert Einstein, São Paulo, São Paulo 05652, Brazil.
5Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada.
6AIMI Center, Stanford University, Stanford, CA 94304, USA.
7CARPL.ai, New Delhi, Delhi 110016, India.
8Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
Abstract
Artificial intelligence (AI) models for automatic generation of narrative radiology reports from images have the potential to enhance efficiency and reduce the workload of radiologists. However, evaluating the correctness of these reports requires metrics that can capture clinically pertinent differences. In this study, we investigate the alignment between automated metrics and radiologists' scori…
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