Medical AI Agents for Clinical Decision Support: Viewpoint Using the Planning, Action, Reflection, and Memory (PARM) Analytical Lens
TL;DR
This Viewpoint argues that agentic architectures incorporating planning, action, reflection, and memory (PARM) represent a meaningful evolution beyond traditional rule-based, machine learning, and multimodal clinical decision support systems.
Abstract
Abstract Medical AI agents are emerging as a new generation of clinical decision support systems, moving beyond static prediction toward multistep, workflow-oriented assistance. This Viewpoint argues that agentic architectures incorporating planning, action, reflection, and memory (PARM) represent a meaningful evolution beyond traditional rule-based, machine learning, and multimodal clinical decision support systems. Using PARM as an analytical lens, we examine how medical AI agents can support diagnostic reasoning, treatment planning, and longitudinal monitoring while remaining constrained by human oversight. We further discuss the governance mechanisms required for responsible implementation, including bounded autonomy, auditability, verification protocols, postdeployment surveillance, and clear accountability structures. Rather than proposing autonomous modification of clinical judgment, this Viewpoint emphasizes agentic AI as a supervised workflow support paradigm. Safe implementation will require technical safeguards, institutional governance, regulatory clarity, and evaluation approaches that assess end-to-end task reliability, escalation behavior, and performance under deployment shifts.