A Review on Artificial Intelligence as a Solution to Burnout in Interventional Radiology.
Authors
Affiliations (10)
Affiliations (10)
- Imperial College London, London, SW7 2AZ, UK. [email protected].
- Imperial College London, London, SW7 2AZ, UK.
- College of Medicine, Alfaisal University, Takhasusi Road, P.O. Box 50927, Riyadh, Saudi Arabia.
- Eastern Health Clinical School, Monash University and Eastern Health, Melbourne, Australia.
- Massachusetts General Hospital, Boston Massachusetts, MA, 02114, USA.
- Neurointerventional Radiology Unit, Monash Health, Melbourne, Australia.
- School of Medicine, Deakin University, Waurn Ponds, Geelong, Australia.
- Department of Interventional Radiology, University Hospital of Strasbourg, Strasbourg, France.
- St George's University Hospitals NHS Foundation Trust and St George's, Blackshaw Road, London, SW17 0QT, UK.
- London North West University Healthcare NHS Trust, A404 Watford Rd, Harrow, HA1 3UJ, UK.
Abstract
We review burnout risk factors in interventional radiology (IR) and explore how artificial intelligence (AI) would address burnout from a workplace aspect. We performed a literature search on PubMed on risk factors for burnout in interventional radiology and AI tools to address burnout challenges. 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. AI may be a solution to addressing specific burnout risk factors in interventional radiology. No level of evidence. Review Article.