{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T07:36:18Z","timestamp":1774683378691,"version":"3.50.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72101211"],"award-info":[{"award-number":["72101211"]}],"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":["81501294"],"award-info":[{"award-number":["81501294"]}],"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":["91846302"],"award-info":[{"award-number":["91846302"]}],"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":["71871065"],"award-info":[{"award-number":["71871065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Clinical Research Plan of SHDC","award":["SHDC2020CR3080B"],"award-info":[{"award-number":["SHDC2020CR3080B"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Although artificial intelligence (AI) has achieved high levels of accuracy in the diagnosis of various diseases, its impact on physicians\u2019 decision-making performance in clinical practice is uncertain. This study aims to assess the impact of AI on the diagnostic performance of physicians with differing levels of self-efficacy under working conditions involving different time pressures.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and methods<\/jats:title>\n                  <jats:p>A 2 (independent diagnosis vs AI-assisted diagnosis) \u00d7 2 (no time pressure vs 2-minute time limit) randomized controlled experiment of multicenter physicians was conducted. Participants diagnosed 10 pulmonary adenocarcinoma cases and their diagnostic accuracy, sensitivity, and specificity were evaluated. Data analysis was performed using multilevel logistic regression.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>One hundred and four radiologists from 102 hospitals completed the experiment. The results reveal (1) AI greatly increases physicians\u2019 diagnostic accuracy, either with or without time pressure; (2) when no time pressure, AI significantly improves physicians\u2019 diagnostic sensitivity but no significant change in specificity, while under time pressure, physicians\u2019 diagnostic sensitivity and specificity are both improved with the aid of AI; (3) when no time pressure, physicians with low self-efficacy benefit from AI assistance thus improving diagnostic accuracy but those with high self-efficacy do not, whereas physicians with low and high levels of self-efficacy both benefit from AI under time pressure.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>This study is one of the first to provide real-world evidence regarding the impact of AI on physicians\u2019 decision-making performance, taking into account 2 boundary factors: clinical time pressure and physicians\u2019 self-efficacy.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>AI-assisted diagnosis should be prioritized for physicians working under time pressure or with low self-efficacy.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocac179","type":"journal-article","created":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T19:06:55Z","timestamp":1665688015000},"page":"2041-2049","source":"Crossref","is-referenced-by-count":26,"title":["How does the artificial intelligence-based image-assisted technique help physicians in diagnosis of pulmonary adenocarcinoma? 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