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.
The research aimed to determine whether AI, often suggested as a solution for managing radiology workloads, actually alleviates or exacerbates burnout. The cross-sectional study included 6,726 radiologists from 1,143 hospitals from China, assessing burnout across three dimensions: emotional exhaustion, depersonalization, and personal accomplishment. The results revealed that frequent AI users had a notably higher prevalence of burnout (40.9%) compared to non-AI users (38.6%), primarily due to emotional exhaustion. The link between AI usage and burnout was especially pronounced among radiologists with heavy workloads or low acceptance of AI.
Although this research is only based on surveys, it raises important considerations. Does the efficiency AI promises for radiologists end up creating additional workload that outweighs its intended benefits in reducing overall workload? Is the problem inherent to AI, or is it more about how we implement the technology and train users to use it effectively? Read more for a deeper dive into this intriguing relationship.
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Artificial Intelligence and Radiologist Burnout
JAMA Network Open, 2024
Abstract
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.