ArticleEuropean radiology2025
Burnout crisis in Chinese radiology: will artificial intelligence help?
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Generative AI Use, Perceived Usefulness, Perceived Risk, and Physician Burnout and Fulfillment Among Chinese Physicians: Mixed Methods Multiregional Study.Journal of medical Internet research · 2026Article
- Letter to the Editor: Radiologist burnout and AI's true black box-job-crafting as the missing piece.European radiology · 2026Article
- Between automation and alienation: rethinking AI's role in radiologist well-being.European radiology · 2026Article
- Radiologist burnout: AI's true black box.European radiology · 2026Review
- Large language model-assisted radiology reporting in a single-radiologist implementation: a retrospective cohort study interpreted through a UTAUT lens.Abdominal radiology (New York) · 2026Article
- Predicting burnout in radiology nurses: an interpretable machine learning model developed and externally validated in a multi-center study.Frontiers in public health · 2026Article
- Different artificial neural networks for predicting burnout risk in Italian anesthesiologists.Journal of anesthesia, analgesia and critical care · 2025Article
- Work stress and burnout among radiology nurses: a cross-sectional study on the mediating role of effort-reward imbalance.Frontiers in public health · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectivesTo assess the correlation between the use of artificial intelligence (AI) software and burnout in the radiology departments of hospitals in China.
methodsThis study employed a cross-sectional research design. From February to July 2024, an online survey was conducted among radiologists and technicians at 68 public hospitals in China. The survey utilized general information questionnaires, the Maslach Burnout Inventory-Human Services Survey (MBI-HSS) scale, and a custom-designed AI usage questionnaire. This study analyzed the correlation between AI software usage and occupational burnout, and general information was included as a control variable in a multiple linear regression analysis.
resultsThe analysis of survey data from 522 radiology staff revealed that 389 (74.5%) had used AI and that 252 (48.3%) had used it for more than 12 months. Only 133 (25.5%) had not yet adopted AI. Among the respondents, radiologists had a higher AI usage rate (82.0%) than technicians (only 59.9%). Furthermore, 344 (65.9%) of the respondents exhibited signs of burnout. The duration of AI software usage was significantly negatively correlated with overall burnout, yielding a Pearson correlation coefficient of -0.112 (p < 0.05). Multiple stepwise regression analysis revealed that salary satisfaction, night shifts, duration of AI usage, weekly working hours, having children, and professional rank were the main factors influencing occupational burnout (all p < 0.05).
conclusionAI has the potential to significantly help mitigate occupational burnout among radiology staff. This study reveals the key role that AI plays in assisting radiology staff in their work. KEY POINTS: Questions Although we are aware that radiology staff burnout is intensifying, there is no quantitative research assessing whether artificial intelligence software can mitigate this occupational burnout. Findings The longer staff use deep learning-based artificial intelligence imaging software, the less severe their occupational burnout tends to be. This result is particularly evident among radiologists. Clinical relevance In China, radiologists and technicians experience high burnout rates. Even if there is an artificial intelligence usage controversy, encouraging the use of artificial intelligence software in radiology helps prevent and alleviate this occupational burnout.
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