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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.
Cardiovascular and Interventional Radiology
|April 24, 2026
Summary
Artificial intelligence (AI) can help reduce burnout among interventional radiology (IR) specialists by addressing workplace factors. AI tools can identify burnout, ease administrative tasks, and improve workflow efficiency.
Area of Science:
- Medical Informatics
- Radiology
- Artificial Intelligence
Background:
- Interventional radiology (IR) specialists experience significant burnout.
- Burnout risk factors exist at personal, workplace, and systemic levels within IR.
Purpose of the Study:
- To review burnout risk factors in interventional radiology.
- To explore the potential of artificial intelligence (AI) in mitigating workplace-related burnout in IR.
Main Methods:
- Literature search on PubMed.
- Focused on risk factors for burnout in IR and AI tools for addressing burnout challenges.
- Review article, no level of evidence assigned.
Main Results:
- AI can identify burnout through analysis of demographic data and free text.
- AI can alleviate administrative workload and optimize workflow management.
- AI may enhance procedural efficiency and reduce radiation exposure-related stress.
Conclusions:
- Artificial intelligence presents a potential solution for specific burnout risk factors in interventional radiology.
- Future directions include AI-driven burnout identification and improved medical coding for longitudinal data access.
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