AI and Blockchain for Secure and Decentralized Healthcare Systems

The swift digitalization of the healthcare industry has further raised concerns regarding data security, patient confidentiality, and operational inefficiency in centralized systems. In response to these issues, this research creates a decentralized framework for healthcare that combines blockchain and artificial intelligence (AI) for secure and privacy-preserving data management. The suggested architecture utilizes blockchain-based modular architecture for the storage of Electronic Health Records (EHRs), federated learning for AI model training without sharing raw patient data, and smart contracts to provide automated access control. The protection of data is also added through encryption and secure multi-party computation methods. Experimental simulations illustrate that the system attains a $17.6 \%$ improvement in transaction success rate, a $12.4 \%$ boost in AI model accuracy, and a $21.3 \%$ decrease in latency over centralized baselines without compromising strong privacy guarantees. In the face of computational overhead and scalability issues, the results affirm the efficiency and robustness of the framework in closing essential gaps in healthcare data management. This research adds a new, patient-centric solution that enhances the vision of secure, intelligent, and interoperable digital health ecosystems.