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Artificial Intelligence and Radiologist Burnout.

Hui Liu1, Ning Ding2,3, Xinying Li4

  • 1Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

JAMA Network Open
|November 22, 2024
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This summary is machine-generated.

Frequent artificial intelligence (AI) use in radiology is linked to higher radiologist burnout, especially with heavy workloads or low AI acceptance. Further research is needed to understand this association.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Occupational Health

Background:

  • Understanding the impact of artificial intelligence (AI) on physician well-being is essential for effective human-AI collaboration.
  • Physician burnout is a significant concern in healthcare, affecting job satisfaction and patient care.

Purpose of the Study:

  • To investigate the association between the use of artificial intelligence (AI) in radiology and the prevalence of radiologist burnout.
  • To explore how workload and AI acceptance moderate the relationship between AI use and burnout.

Main Methods:

  • A cross-sectional survey was conducted among 6726 radiologists in China.
  • Radiologists were categorized into AI and non-AI groups based on regular AI use in practice.
  • Burnout was assessed using the Maslach Burnout Inventory, with statistical analyses performed using propensity score-based regression.

Main Results:

  • The prevalence of burnout was higher in the AI group (40.9%) compared to the non-AI group (38.6%).
  • AI use was significantly associated with increased odds of burnout (OR, 1.20), primarily driven by emotional exhaustion.
  • A dose-response relationship was observed between AI use frequency and burnout, with stronger associations in radiologists facing high workloads or low AI acceptance.

Conclusions:

  • Frequent AI use in radiology is associated with an elevated risk of radiologist burnout.
  • This association is particularly pronounced for radiologists with high workloads and lower acceptance of AI technologies.
  • Longitudinal studies are recommended to further elucidate the causal relationship and develop targeted interventions.