Abstract

Delivering effective predictive modeling for patient care using AI requires a synergistic blend of advanced machine learning techniques, rigorous data security protocols, and strong ethical oversight to safeguard patient privacy and ensure regulatory compliance. We propose a framework that leverages federated learning, enabling models to train on decentralized, de-identified datasets without transferring sensitive patient information. This approach supports predictions of disease risk, personalized treatment recommendations, and optimized resource allocation across healthcare systems. To enhance data security, we incorporate multi-party computation and homomorphic encryption, allowing encrypted data to be processed without compromising confidentiality. AI-powered real-time anomaly detection systems will monitor for cybersecurity threats such as unauthorized access or data breaches, further reinforcing the secure infrastructure. Additionally, explainable AI (XAI) will be integrated to foster transparency, enabling clinicians to understand and trust model outputs. By combining privacy-preserving AI methods, secure computation, and continuous threat monitoring, this approach empowers healthcare providers to harness AI-driven insights while upholding the highest standards of security, accountability, and patient trust.