{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T01:56:51Z","timestamp":1793325411297,"version":"4.5.0"},"reference-count":40,"publisher":"American Medical Association (AMA)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n IMPORTANCE<\/jats:title>\n Understanding the association of artificial intelligence (AI) with physician burnout is crucial for fostering a collaborative interactive environment between physicians and AI.<\/jats:p>\n <\/jats:sec>\n \n OBJECTIVE<\/jats:title>\n To estimate the association between AI use in radiology and radiologist burnout.<\/jats:p>\n <\/jats:sec>\n \n DESIGN, SETTING, AND PARTICIPANTS<\/jats:title>\n This cross-sectional study conducted a questionnaire survey between May and October 2023, using the national quality control system of radiology in China. Participants included radiologists from 1143 hospitals. Radiologists reporting regular or consistent AI use were categorized as the AI group. Statistical analysis was performed from October 2023 to May 2024.<\/jats:p>\n <\/jats:sec>\n \n EXPOSURE<\/jats:title>\n AI use in radiology practice.<\/jats:p>\n <\/jats:sec>\n \n MAIN OUTCOMES AND MEASURES<\/jats:title>\n Burnout was defined by emotional exhaustion (EE) or depersonalization according to the Maslach Burnout Inventory. Workload was assessed based on working hours, number of image interpretations, hospital level, device type, and role in the workflow. AI acceptance was determined via latent class analysis considering AI-related knowledge, attitude, confidence, and intention. Propensity score\u2013based mixed-effect generalized linear logistic regression was used to estimate the associations between AI use and burnout and its components. Interactions of AI use, workload, and AI acceptance were assessed on additive and multiplicative scales.<\/jats:p>\n <\/jats:sec>\n \n RESULTS<\/jats:title>\n \n Among 6726 radiologists included in this study, 2376 (35.3%) were female and 4350 (64.7%) were male; the median (IQR) age was 41 (34-48) years; 3017 were in the AI group (1134 [37.6%] female; median [IQR] age, 40 [33-47] years) and 3709 in the non-AI group (1242 [33.5%] female; median [IQR] age, 42 [34-49] years). The weighted prevalence of burnout was significantly higher in the AI group compared with the non-AI group (40.9% vs 38.6%;\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001). After adjusting for covariates, AI use was significantly associated with increased odds of burnout (odds ratio [OR], 1.20; 95% CI, 1.10-1.30), primarily driven by its association with EE (OR, 1.21; 95% CI, 1.10-1.34). A dose-response association was observed between the frequency of AI use and burnout (\n P<\/jats:italic>\n for trend\u2009&amp;lt;\u2009.001). The associations were more pronounced among radiologists with high workload and lower AI acceptance. A significant negative interaction was noted between high AI acceptance and AI use.\n <\/jats:p>\n <\/jats:sec>\n \n CONCLUSIONS AND RELEVANCE<\/jats:title>\n In this cross-sectional study of radiologist burnout, frequent AI use was associated with an increased risk of radiologist burnout, particularly among those with high workload or lower AI acceptance. Further longitudinal studies are needed to provide more evidence.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2024.48714","type":"journal-article","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T11:31:36Z","timestamp":1732275096000},"page":"e2448714","source":"Crossref","is-referenced-by-count":104,"title":["Artificial Intelligence and Radiologist Burnout"],"prefix":"10.1001","volume":"7","author":[{"given":"Hui","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Ding","sequence":"additional","affiliation":[{"name":"Radiology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China"},{"name":"National Center for Quality 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