Review uncovers burnout risk factors in interventional radiology, suggesting AI as a potential solution.
Purpose We review burnout risk factors in interventional radiology (IR) and explore how artificial intelligence (AI) would address burnout from a workplace aspect. Materials and Methods We performed a literature search on PubMed on risk factors for burnout in interventional radiology and AI tools to address burnout challenges. Results IR specialists face burnout risk at personal, workplace and system levels. AI could identify burnout using demographic data and free text, alleviate administrative workload, and manage workflow. AI could also enhance procedural efficiency via automated navigation systems, reducing stress from radiation exposure. Future directions include enhanced burnout identification and medical coding for access to longitudinal data. Conclusion AI may be a solution to addressing specific burnout risk factors in interventional radiology. Level of Evidence No level of evidence. Review Article.
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Zhang et al. (2026) studied this question.
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