Agentic AI-Powered Automation: A New Paradigm for Healthcare Workflow Optimization

Abstract

The approach to AI-driven healthcare automation, which is being introduced using agentic AI, is a revolutionary one that uses self-directing and context-sensitive agents with decision-making abilities assigned to them so that they can carry out the activities. This paper is devoted to the idea of the improvement of workflow efficiency, accuracy, and scale in clinical and administrative settings offered by multi-agent systems. Using case studies of experimental systems and frameworks (such as HIPAA-compliant agents and triage based on the use of LLMs), we can evaluate the usefulness of real-time decision-making assistance and teamwork among lunar AI. Among the key metrics, one may speak of faster prediction accuracy, decreased latency, and transparency. We should consider that our findings indicate that agentic AI sets a paradigmatic change towards intelligent, adaptive healthcare processes that are efficient and compliant to safety and regulations guidelines.