Cross-Vertical Integration of AI, Blockchain, and IoT for Secure and Resilient Digital Health Ecosystems

The convergence of AI, Blockchain, and IoT technologies is reshaping cybersecurity paradigms across digital health ecosystems. This study investigates their integration as a unified framework to address cross-sector security challenges, improve risk detection, and enhance data protection. AI mechanisms enable real-time threat identification and adaptive response through machine learning, while Blockchain ensures integrity, traceability, and decentralized governance of medical data. IoT devices, critical for healthcare automation and remote monitoring, are secured through embedded anomaly detection and robust encryption schemes. A set of structured tables and diagrams outlines the roles, interactions, and technical dependencies of these technologies in layered cybersecurity architectures. In particular, we propose and analyze a reference model combining AI agents, secure IoT data channels, and Blockchain-based authorization protocols. The architecture supports autonomous response loops and cross-domain coordination. This work also identifies implementation challenges such as interoperability, resource constraints, and adversarial AI risks. The findings demonstrate how autonomous, explainable, and trust-enhancing security models can contribute to the sustainable and resilient digital transformation of the healthcare sector and other critical verticals.