AI-Driven Diagnosis and Treatment Recommendation in Healthcare: A Hybrid Deep Learning Framework

Abstract

The healthcare industry faces persistent challenges in delivering accurate, scalable, and privacy-compliant diagnosis and treatment recommendations. This paper presents an innovative hybrid AI framework that integrates deep learning, federated learning, and reinforcement learning to provide an automated, explainable, and secure medical decision-making system. Unlike traditional AI models, our approach ensures data privacy through differential privacy and secure multi-party computation, while an explainable AI (XAI) module enhances clinician trust by making predictions interpretable. The system dynamically personalizes treatment recommendations using hierarchical reinforcement learning, optimizing chronic disease management. We validated the framework on real-world multi-modal medical datasets, including MIMIC-IV, NIH X-ray, and PubMed Genetics. The system processes multiple data modalities, such as electronic health records (EHRs), medical imaging (X-rays, MRIs), and genetic data, achieving an 18% reduction in diagnostic errors and a 25% improvement in patient outcomes. This research sets a new benchmark in AI-driven healthcare by demonstrating a scalable, privacy-preserving, and clinically interpretable system that can be seamlessly integrated into modern hospital infrastructures.

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