This study explores the growing transition in healthcare from rigid, rule-based algorithms to intelligent, self-directed “agentic” AI systems. These agents rely on Large Language Models (LLMs) to think step-by-step, make decisions, and carry out tasks using a simple yet powerful four-part structure: planning, action, reflection, and memory.

The paper reviews their current uses in medical diagnosis, hospital workflow automation, and collaborative multi-agent setups. It also highlights a major challenge — most testing still happens in controlled lab settings rather than real hospitals — and discusses what is needed for safe everyday adoption.

Keywords: Medical AI Agents, Autonomous Clinical Systems, Large Language Models, Chain-of-Thought Reasoning, Multimodal Integration, Agentic Framework, Clinical Decision Support, Human-in-the-Loop, Healthcare Automation, Physician Burnout, Precision Medicine, Scoping Review, Simulation Gap, PRISMA-ScR.

Healthcare AI agents support several key areas. They strengthen diagnostic accuracy in critical situations such as predicting sepsis or identifying cancer on scans. They lighten the administrative load by automatically creating summaries from patient records, which helps reduce doctor burnout. They also improve patient involvement by offering personalised guidance, voice-based recovery check-ins, and easier remote care. In addition, groups of specialist agents can work together on complicated cases, such as building complete treatment plans.