AI-Driven Digital Health: Pioneering Innovations, Overcoming Challenges, and Shaping Future Frontiers

Abstract

Artificial Intelligence (AI) is rapidly transforming digital healthcare by enabling data-driven, adaptive, and scalable interventions across the continuum of care. However, challenges related to patient data privacy, model transparency, and interoperability remain significant-especially when deploying AI systems in multi-institutional or low-resource settings. In this work, we propose HealthEdge-AI, a federated and privacy-preserving transformer-based framework designed to support clinical decision-making in real time while ensuring compliance with global healthcare data protection regulations. The proposed system integrates three core innovations: (i) federated learning to enable decentralized model training across siloed datasets without transmitting raw patient information, (ii) multimodal data fusion using transformer architectures to process electronic health records (EHRs), physiological sensor streams, and medical images, and (iii) explainable AI (XAI) modules that generate human-interpretable insights via attention maps and SHAP-based reasoning. HealthEdge-AI embeds differential privacy and homomorphic encryption within its communication protocols to achieve strong formal guarantees of security and confidentiality. Empirical validation was performed using three benchmark datasets (MIMIC-IV, PhysioNet, and MedMNIST), targeting the diagnosis and risk stratification of non-communicable diseases (NCDs) including cardiovascular conditions and diabetes. The framework consistently outperformed baseline centralized and federated models in terms of accuracy (↑7.8%), privacy compliance ( < 2.0), and explainability scores. Results demonstrate the potential of HealthEdge-AI as a scalable and ethically grounded platform for enabling equitable healthcare innovation, particularly in cross-institutional and resource-constrained environments

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