There has been an increase in the number of imaging studies performed worldwide over the past 2 decades. 1 There has been a simultaneous increase in the number of artificial intelligence (AI) models approved by the Food and Drug Administration (FDA) in the United States, with over 75% of these models approved for use in radiology. 2 AI has been used to assist with interpretative tasks (detection, diagnosis, prognosis) and noninterpretive tasks (creating reports, protocols, contacting ordering clinicians, scheduling) in radiology. It is reasonable to assume that AI would be a useful adjunct for radiologists, increase radiologist efficiency, and decrease radiologist burnout. However, some reports, including the report by Liu et al, 3 suggest that this may not be true. Burnout is a syndrome caused by unmanaged chronic workplace stress, characterized by 3 main dimensions: (1) energy depletion or exhaustion, (2) increased mental detachment or negativity toward one's job, and (3) a sense of ineffectiveness and lack of achievement. 3 The burnout rate in radiologists is high. 4,5 Liu et al 3 evaluated the association between AI use and radiologist burnout. To do this, they analyzed data from an online self-administered Nationwide Maslach Burnout Inventory-Human Services Survey on 6726 radiologists (89.4% response rate), aged 20 to 74 years of age, from 1143 hospitals in China. Authors defined burnout as an emotional exhaustion score (EE Ն27), or a depersonalization score (DP Ն10). Radiologists were stratified based on AI usage into an AI group (regularly/consistently) (n = 3017) and non-AI group (never/infrequently) (n = 3709). Latent class analysis of the technology acceptance model identified 2 distinct latent classes: a low acceptance class (limited knowledge, negative attitude, low intention, and high confidence) and a high acceptance class (extensive knowledge, objective attitude, high intention). To examine the association between AI use and burnout, the authors used propensity score-based multivariable logistic regression. They assessed workload (working hours on image interpretation, the amount of image interpretation, device type, role in the reporting workflow, and hospital level) and categorized radiologists into 3 groups based on their scores: low (0-2), medium (3), and high (4-5), which assumes a linear relationship between scores. Personal and professional characteristics were gathered through a self-designed questionnaire while researchers evaluated radiologists based on psychological factors using the Gallup Q12 Employee Engagement scale, measuring perceived control, spiritual rewards, work values, organizational support, and coworker support. The authors examined the association between AI use and burnout, adjusting for personal and professional characteristics, workload score, AI acceptance, and psychological factors. AI use was treated as both categorical and continuous to show the dose-response association.The use of AI was associated with higher odds of burnout among radiologists. This association ex...
