Agentic AI for Healthcare: Solutions to Intelligent Patient Care

Abstract

The advancement of artificial intelligence (AI) in healthcare has opened new avenues for improving patient out-comes through enhanced diagnostic accuracy, optimized treatment strategies, and more efficient clinical workflows. This study presents a novel Agentic AI system designed to address key inefficiencies in modern healthcare systems. Unlike conventional AI approaches, the proposed system integrates adaptive decision-making mechanisms that dynamically respond to patient conditions, ensuring real-time anomaly detection, predictive diagnostics, and intelligent intervention strategies. To validate its effectiveness, the model was tested across three critical healthcare scenarios. The early disease detection module achieved a predictive accuracy of 92.3%, outperforming traditional risk models. The real-time patient monitoring system demonstrated an anomaly detection rate of 98.7%, with an alert response time of just 2.5 seconds, showcasing its reliability in clinical settings. Moreover, the proposed system outperformed existing AI solutions, attaining a 96.5% confidence score, reducing misdiagnosis rates by 22%, and surpassing traditional radiologists in both sensitivity and specificity. This work bridges the gap between theoretical AI advancements and real-world healthcare applications, laying the foundation for intelligent, autonomous systems in modern medical practice.

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