Agent AI for Personalized Healthcare: A Multi-Agent Framework for Real-Time Disease Detection and Patient Support

Abstract

The rapid growth of digital health data from wearable devices, electronic health records, and medical imaging has created unprecedented opportunities for personalized healthcare. However, traditional AI models often face limitations in scalability, adaptability, and interpretability, which restrict their integration into real-time clinical decisionmaking. This paper proposes an Agentic AI-driven multi-agent framework for personalized healthcare that unifies disease detection, patient monitoring, and clinical decision support. The architecture leverages specialized agents-including perception agents for data collection, diagnostic agents for disease prediction, and support agents for patient engagementcoordinated through reasoning, collaboration, and human-in-the-loop governance layers. Reinforcement learning and federated learning modules enhance adaptability and scalability across distributed healthcare systems, while explainable AI (XAI) techniques ensure transparency and trust in critical medical decisions. A taxonomy of agent roles and layered technical solution architecture are presented to illustrate system design. Applications across chronic disease management, preventive care, and emergency response demonstrate the framework's effectiveness in real-time scenarios. Challenges such as data privacy, interoperability, adversarial robustness, and ethical governance are analyzed, along with emerging solutions including blockchain integration and neuro-symbolic reasoning. This work positions Agent AI as a transformative paradigm for delivering secure, adaptive, and patient-centric healthcare.

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