Agentic AI in Healthcare: Opportunities, Challenges, and Future Directions

The integration of Agentic AI into healthcare marks a new era in medical automation and decision support. Leveraging autonomous reasoning, planning, and collaboration, these AI systems are transforming clinical workflows, diagnostics, and patient management. Recent advancements have led Agentic AI to evolve from single-agent decision-making to multi-agent systems, enabling more sophisticated problem-solving in complex medical environments. In this survey, we provide an in-depth discussion on the core aspects and challenges of Agentic AI in healthcare, including its applications in electronic health records (EHR) interactions, clinical triage, medical question-answering, and disease diagnosis. We categorize existing single-agent and multi-agent frameworks, explore key evaluation metrics, implementation strategies, and commonly used benchmarks, and examine the communication, reasoning, and decision-making processes of these AI agents. Additionally, we address critical challenges such as model reliability, interoperability, ethical considerations, and regulatory constraints. To support further research, we summarize relevant datasets and benchmarks and we outline future research directions that emphasize human-AI collaboration, transparency, and the safe deployment of AI-driven medical systems.