Authors: Sonawane Shubhangi Chintaman, Bangar Abhishek Popat, Bhosale Sachin Bajirao
Abstract: Healthcare organisations are exploring AI systems that do more than answer questions: they plan multi-step tasks, retrieve evidence, call software tools and coordinate with other agents. This capability promises support for clinical decision-making and administration, yet it widens the security and safety exposure of systems that handle highly sensitive patient data. This paper presents a structured narrative review of five verified IEEE journal articles published in 2025–2026. They cover the foundations of agentic AI, a taxonomy-based survey of healthcare agents, a multi-agent clinical decision support system, a multi-agent framework for Alzheimer's disease analysis and a privacy-preserving language-model framework for medical consultation. The papers are compared on objectives, methods, contributions, limitations and relevance to secure healthcare. The evidence indicates that retrieval-grounded, multi-agent designs are the dominant pattern, whereas human-in-the-loop control, adversarial testing, drift monitoring and regulatory compliance are rarely implemented. In the parts of the sources that could be read, no prospective clinical outcome trial is reported. The paper contributes a thematic synthesis, a five-study comparison, a conceptual layered architecture assembled from safeguards found in the literature, and a research agenda. The review is deliberately narrow and makes no claim of systematic coverage.
