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Summary

Autonomous agent systems, a leap beyond large language models, offer proactive clinical assistance by integrating memory, knowledge retrieval, and computer use. These AI systems can coordinate complex clinical workflows, transforming healthcare delivery.

Keywords:
Agentic AIArtificial IntelligenceClinical Decision SupportHealth Care AutomationImpact of AI on EducationInformaticsLarge Language ModelsMulti-Agent SystemsNamed Entity RecognitionPatient Scheduling/No-Show PredictionRadiology WorkflowResource Allocation

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Workflow Automation
  • Healthcare Technology Transformation

Background:

  • Foundational models, particularly large language models, have initiated healthcare transformation.
  • The field is shifting towards autonomous agent systems for proactive, goal-oriented clinical assistance, moving beyond passive information retrieval.

Purpose of the Study:

  • To explore the paradigm shift from passive information retrieval to proactive, goal-oriented clinical assistance using autonomous agent systems.
  • To outline the capabilities, workflows, and implementation considerations for agentic AI in healthcare.

Main Methods:

  • Agentic AI systems leverage persistent memory, retrieval-augmented generation for medical knowledge, and computer use functionality.
  • Multiagent systems demonstrate coordinated workflows (hierarchical, collaborative, sequential) for tasks across the radiology lifecycle.
  • A four-phase implementation roadmap is proposed for incremental deployment and safety.

Main Results:

  • Agentic AI systems can autonomously coordinate entire clinical workflows, from preacquisition to preliminary report generation.
  • Multiagent systems show superior performance compared to single-agent approaches in complex tasks.
  • Successful deployment necessitates addressing complexity, economic sustainability, cybersecurity, bias, and governance.

Conclusions:

  • Agentic AI represents a significant advancement, capable of reshaping radiology practice paradigms.
  • Implementation requires careful management of probabilistic models in deterministic workflows and ensuring human supervision.
  • The future success hinges on resolving stakeholder responsibilities and integrating AI capabilities with clinical accountability for improved patient outcomes.