Artificial Intelligence and Radiologist Burnout

doi:10.1001/jamanetworkopen.2024.48714

PSYCH OpenIR

	Artificial Intelligence and Radiologist Burnout
	Liu, Hui 1; Ding, Ning 2,3; Li, Xinying 4; Chen, Yunli 1; Sun, Hao 2,3; Huang, Yuanyuan 1; Liu, Chen 5; Ye, Pengpeng 6; Jin, Zhengyu 2,3; Bao, Heling 1; Xue, Huadan 2,3
通讯作者	Bao, Heling(bao.heling@imicams.ac.cn) ; Xue, Huadan(bjdanna95@163.com)
摘要	IMPORTANCE Understanding the association of artificial intelligence (AI) with physician burnout is crucial for fostering a collaborative interactive environment between physicians and AI. OBJECTIVE To estimate the association between AI use in radiology and radiologist burnout. DESIGN, SETTING, AND PARTICIPANTS 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. EXPOSURE AI use in radiology practice. MAIN OUTCOMES AND MEASURES 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-based 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. RESULTS 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%; P < .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 (P for trend < .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. CONCLUSIONS AND RELEVANCE 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.
	2024-11-22
语种	英语
DOI	10.1001/jamanetworkopen.2024.48714
发表期刊	JAMA NETWORK OPEN
ISSN	2574-3805
卷号	7 期号:11 页码:13
收录类别	SCI
资助项目	Chinese Academy Medical Sciences Innovation Fund for MedicalSciences
出版者	AMER MEDICAL ASSOC
WOS关键词	PREVALENCE
WOS研究方向	General & Internal Medicine
WOS类目	Medicine, General & Internal
WOS记录号	WOS:001361876300014
资助机构	Chinese Academy Medical Sciences Innovation Fund for MedicalSciences
引用统计
文献类型	期刊论文
条目标识符	https://ir.psych.ac.cn/handle/311026/48672
通讯作者	Bao, Heling; Xue, Huadan
作者单位	1.Chinese Acad Med Sci & Peking Union Med Coll, Inst Med Informat, 3 Yabao Rd, Beijing 100020, Peoples R China 2.Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Dept Radiol, State Key Lab Complex Severe & Rare Dis, Rd Shuaifuyuan, Beijing 100730, Peoples R China 3.Natl Ctr Qual Control Radiol, Rd Shuaifuyuan, Beijing 100730, Peoples R China 4.Chinese Acad Sci, Key Lab Mental Hlth, Inst Psychol, Beijing, Peoples R China 5.Beijing United Family Hosp, Psychol Hlth Ctr, Beijing, Peoples R China 6.Chinese Ctr Dis Control & Prevent, Natl Ctr Noncommunicable Dis Control & Prevent, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Liu, Hui,Ding, Ning,Li, Xinying,et al. Artificial Intelligence and Radiologist Burnout[J]. JAMA NETWORK OPEN,2024,7(11):13.
APA	Liu, Hui.,Ding, Ning.,Li, Xinying.,Chen, Yunli.,Sun, Hao.,...&Xue, Huadan.(2024).Artificial Intelligence and Radiologist Burnout.JAMA NETWORK OPEN,7(11),13.
MLA	Liu, Hui,et al."Artificial Intelligence and Radiologist Burnout".JAMA NETWORK OPEN 7.11(2024):13.