Secure AI Agent–Driven Conversational Support in Healthcare Integrating Cybersecurity from Diagnostics to Patient Coaching

Abstract

Industrial American styles of capitalism have reified AI as an industry of speculative economy, thereby accelerating the agency with which these agents are adopted into systems of care and further deferred responsibility for patient engagement, clinical diagnostics, and personalized coaching. But that transformation carries significant cyber risks, potentially compromising the integrity and privacy of patient data as well system reliability. Here we propose a broad framework for secure AI agent–aided conversational support through the health care continuum, from intelligent diagnostics to continuous patient coaching. Here we propose a detailed security architecture utilizing end-to-end encryption, federated learning, role-based access control and real-time anomaly detection to protect conversational AI pipelines. This solidly honors upholding regulatory integrity like GDPR and HIPAA frameworks with the seamless patient experience continuity powered by intelligence. By assessing unique threat vectors tailored towards healthcare conversational agents (e.g. adversarial prompt injection data poisoning and the model inversion attack), we detail a concrete strategy for how these active cybersecurity approaches can be incorporated into the system with no adverse impact on diagnostic precision or user experience. Our method yields strong threat mitigation and low latency overhead, importantly instilling trustworthiness of an AI-enabled patient support as experimentally validated. This work proposed a scalable, interoperable solution to safeguard digital health ecosystems against emerging AI capabilities and addresses the critical relationship between AI advances and cybersecurity in healthcare.