Medical AI Agents for Clinical Decision Support: Viewpoint Using the Planning, Action, Reflection, and Memory (PARM)...

Area of Science:

• Artificial Intelligence in Medicine
• Clinical Decision Support Systems
• Healthcare Informatics

Background:

• Emerging medical AI agents represent a shift from static prediction to dynamic, workflow-oriented clinical decision support.
• Traditional systems (rule-based, ML, multimodal) have limitations in complex clinical reasoning and multistep tasks.

Purpose of the Study:

• To introduce agentic architectures incorporating Planning, Action, Reflection, and Memory (PARM) as an evolution in medical AI.
• To analyze how PARM-based AI agents can support diagnostic reasoning, treatment planning, and patient monitoring.
• To discuss governance mechanisms for responsible implementation of agentic AI in healthcare.

Main Methods:

• Conceptual analysis using the PARM framework (Planning, Action, Reflection, Memory).
• Examination of AI agent capabilities in diagnostic, therapeutic, and monitoring workflows.
• Discussion of governance elements: bounded autonomy, auditability, verification, surveillance, and accountability.

Main Results:

• Agentic AI architectures with PARM offer a more sophisticated approach to clinical decision support than existing methods.
• These agents can assist in complex clinical tasks while maintaining human oversight.
• Effective governance structures are crucial for safe and responsible deployment.

Conclusions:

• Agentic AI, particularly with PARM, represents a supervised paradigm for enhancing clinical workflows, not autonomous judgment.
• Safe implementation necessitates robust technical safeguards, institutional governance, regulatory frameworks, and comprehensive evaluation.