Exploring Agentic AI in Healthcare: A Study on Its Working Mechanism

Abstract

Introduction: Rapid advancements in artificial intelligence (AI) have ushered in an era of hyperautomation and intelligent orchestration across multiple engineering domains, with healthcare emerging as one of the most impactful application areas. Among recent developments, Agentic AI has gained attention as a sub-domain of AI capable of autonomous operation, decision-making, and goal-driven behavior with minimal human intervention. This study aims to explore the architectural and functional role of Agentic AI in modern healthcare systems. Methods: The study adopts a conceptual and analytical approach to examine the core components of Agentic AI, including agent design, decision-making mechanisms, task allocation strategies, agent coordination, and ranking frameworks. It further investigates the integration of emerging 6G networking technologies within Agentic AI architectures. A qualitative case study on remote robotic surgery is presented to illustrate practical applicability. Additionally, a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis is conducted to assess strategic and operational considerations. Results: The analysis demonstrates that Agentic AI architectures, when supported by high-speed and low-latency 6G communication, can enable efficient autonomous decision-making and coordinated task execution in complex healthcare workflows. The case study highlights the feasibility of Agentic AI in enabling remote robotic surgery with enhanced responsiveness, precision, and reliability. The SWOT analysis reveals strong potential for scalability and efficiency while also identifying challenges related to ethical governance, system robustness, and security. Discussion: The findings suggest that Agentic AI represents a promising paradigm for next-generation healthcare systems, particularly in remote and critical care applications. While the proposed framework offers architectural insights and strategic value, responsible integration requires addressing limitations such as trust, regulatory compliance, and system transparency. Overall, this study provides a holistic understanding of how Agentic AI can be effectively and ethically integrated into healthcare ecosystems.

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