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A Review on Artificial Intelligence as a Solution to Burnout in Interventional Radiology
Henry Zhang1, Aria Torkpour2, Boroumand Zeidaabadi2
1Imperial College London, London, SW7 2AZ, UK. henry.zhang20@imperial.ac.uk.
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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