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Purpose <p>We review burnout risk factors in interventional radiology (IR) and explore how artificial intelligence (AI) would address burnout from a workplace aspect.</p> Materials and Methods <p>We performed a literature search on PubMed on risk factors for burnout in interventional radiology and AI tools to address burnout challenges.</p> Results <p>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.</p> Conclusion <p>AI may be a solution to addressing specific burnout risk factors in interventional radiology.</p> Level of Evidence <p>No level of evidence. Review Article.</p> Graphical Abstract <p></p>

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A Review on Artificial Intelligence as a Solution to Burnout in Interventional Radiology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月24日

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  • Henry Zhang,
  • Aria Torkpour,
  • Boroumand Zeidaabadi,
  • Nader Ashraf,
  • Anousha Yazdabadi,
  • Shams I. Iqbal,
  • Hamed Asadi,
  • Roberto Luigi Cazzato,
  • Robert Morgan,
  • Behnam Shaygi

摘要

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.

Graphical Abstract