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Related Experiment Video

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Agentic AI in radiology: emerging potential and unresolved challenges.

Nicholas Dietrich1,2

  • 1Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario M5S 1A8, Canada.

The British Journal of Radiology
|July 24, 2025
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Summary

Agentic artificial intelligence (AI) offers autonomous workflow management in radiology, improving efficiency and triage. Careful development and clinician guidance are crucial for its safe implementation.

Keywords:
agentic AIartificial intelligenceautonomous systemsclinical decision supporthuman-AI collaborationradiology workflowsregulatory affairs

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Traditional AI tools in radiology are user-triggered and passive.
  • Emerging agentic AI represents a paradigm shift towards autonomous systems.
  • These systems can manage workflows, plan tasks, and provide clinical decision support.

Purpose of the Study:

  • To introduce agentic artificial intelligence (AI) as a transformative concept in radiology.
  • To highlight the potential benefits and challenges of agentic AI in clinical settings.
  • To advocate for a clinician-guided approach to the development and implementation of agentic AI.

Main Methods:

  • This is a commentary, not a primary research study.
  • It synthesizes current understanding and potential applications of agentic AI.
  • It discusses early pilot studies and proof-of-concept findings.

Main Results:

  • Agentic AI models demonstrate potential for dynamic study prioritization and tailored recommendations.
  • Autonomous administrative task automation is a key feature.
  • Early evidence suggests utility in high-volume and high-acuity radiology settings.

Conclusions:

  • Agentic AI promises significant gains in efficiency, triage accuracy, and cognitive support for radiologists.
  • Barriers such as clinical validation, regulatory hurdles, and integration challenges require attention.
  • Responsible and clinician-guided implementation is essential for realizing the full potential of agentic AI in radiology.