{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T12:18:39Z","timestamp":1789647519465,"version":"4.0.0"},"reference-count":70,"publisher":"American Association for the Advancement of Science (AAAS)","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81970444"],"award-info":[{"award-number":["81970444"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82300345"],"award-info":[{"award-number":["82300345"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Research"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>\n            <jats:bold>Problem:<\/jats:bold>\n            Chest radiography is a crucial tool for diagnosing thoracic disorders, but interpretation errors and a lack of qualified practitioners can cause delays in treatment.\n            <jats:bold>Aim:<\/jats:bold>\n            This study aimed to develop a reliable multi-classification artificial intelligence (AI) tool to improve the accuracy and efficiency of chest radiograph diagnosis.\n            <jats:bold>Methods:<\/jats:bold>\n            We developed a convolutional neural network (CNN) capable of distinguishing among 26 thoracic diagnoses. The model was trained and externally validated using 795,055 chest radiographs from 13 datasets across 4 countries.\n            <jats:bold>Results:<\/jats:bold>\n            The CNN model achieved an average area under the curve (AUC) of 0.961 across all 26 diagnoses in the testing set. COVID-19 detection achieved perfect accuracy (AUC 1.000, [95% confidence interval {CI}, 1.000 to 1.000]), while effusion or pleural effusion detection showed the lowest accuracy (AUC 0.8453, [95% CI, 0.8417 to 0.8489]). In external validation, the model demonstrated strong reproducibility and generalizability within the local dataset, achieving an AUC of 0.9634 for lung opacity detection (95% CI, 0.9423 to 0.9702). The CNN outperformed both radiologists and nonradiological physicians, particularly in trans-device image recognition. Even for diseases not specifically trained on, such as aortic dissection, the AI model showed considerable scalability and enhanced diagnostic accuracy for physicians of varying experience levels (all\n            <jats:italic>P<\/jats:italic>\n            &lt; 0.05). Additionally, our model exhibited no gender bias (\n            <jats:italic>P<\/jats:italic>\n            &gt; 0.05).\n            <jats:bold>Conclusion:<\/jats:bold>\n            The developed AI algorithm, now available as professional web-based software, substantively improves chest radiograph interpretation. This research advances medical imaging and offers substantial diagnostic support in clinical settings.\n          <\/jats:p>","DOI":"10.34133\/research.0426","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T17:22:41Z","timestamp":1719940961000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":6,"title":["Development and External Validation of an Artificial Intelligence-Based Method for Scalable Chest Radiograph Diagnosis: A Multi-Country Cross-Sectional Study"],"prefix":"10.34133","volume":"7","author":[{"given":"Zeye","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Cardiac Surgery, \rPeking University People\u2019s Hospital, Peking University, Xicheng District, Beijing, China."},{"name":"Department of Structural Heart Disease, National Center for Cardiovascular Disease, China &amp; Fuwai Hospital, \rChinese Academy of Medical Sciences &amp; Peking Union Medical College, Beijing 100037, China."},{"name":"National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing 100037, China."},{"name":"Key Laboratory of Innovative Cardiovascular Devices, \rChinese Academy of Medical Sciences, Beijing 100037, China."},{"name":"National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, \rChinese Academy of Medical Sciences, Beijing 100037, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, \rand Peking Union Medical College, Beijing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengliang","family":"Yin","sequence":"additional","affiliation":[{"name":"Medical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, China."},{"name":"National Engineering Research Center for Medical Big Data Application Technology, Chinese People\u2019s Liberation Army (PLA) General Hospital, Beijing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guojing","family":"Han","sequence":"additional","affiliation":[{"name":"College of Pulmonary &amp; Critical Care Medicine, Chinese PLA General Hospital, Beijing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Che","sequence":"additional","affiliation":[{"name":"Center for Health Policy Research and Evaluation, \rRenmin University of China, Beijing, China."},{"name":"School of Public Administration and Policy, \rRenmin University of China, Beijing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ge","family":"Fan","sequence":"additional","affiliation":[{"name":"Lightspeed &amp; Quantum Studios, Tencent Inc., Shenzhen, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Fuwai Hospital, \rChinese Academy of Medical Sciences &amp; Peking Union Medical College, Beijing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixin","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Pulmonary &amp; Critical Care Medicine, Chinese PLA General Hospital, Beijing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Bao","sequence":"additional","affiliation":[{"name":"Shenzhen Benevolence Medical Sci&amp;Tech Co. 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