Agentic AI–Driven Disease Prediction for Smart Hospital Systems

Traditional Hospital Management Systems (HMS) primarily serve administrative and record-keeping functions but lack any form of autonomous clinical intelligence. With the growing complexity of patient data, healthcare institutions increasingly require systems capable of proactive decision support, early disease detection, and dynamic patient monitoring. This paper presents IntelliHMS 2.0, an innovative hospital management ecosystem enhanced with Agentic AI, a new paradigm where autonomous, goal-driven AI agents operate collaboratively to perform multi-step reasoning, analyze multi-modal patient data, and provide real-time disease prediction. Unlike traditional Machine Learning models that perform static onetime predictions, Agentic AI systems autonomously retrieve data, interpret clinical signals, reason over medical guidelines, generate insights, and trigger appropriate actions. IntelliHMS 2.0 integrates multiple specialized AI agents—including a Data Retrieval Agent, Disease Prediction Agent, Clinical Reasoning Agent, Monitoring Agent, and Explainability Agent—to create a fully autonomous predictive workflow. Using cloud-based microservices and secure API-driven architecture, the system ensures scalability, reliability, and continuous adaptation to patient conditions. By transforming disease prediction from a single-step model into a selfdirected, autonomous diagnostic pipeline, this system significantly improves early risk detection, enhances clinical decision-making, and streamlines overall hospital operations. This research highlights the potential of Agentic AI to revolutionize modern healthcare systems, enabling proactive, intelligent, and adaptive care delivery.

AI, Driven, Hospital, Prediction

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