Distributed Artificial Intelligence and Health Governance: A Multidimensional Analysis of the Tensions Between Rules, Ethics and Innovation

DAI is transforming healthcare systems by enabling collaborative, privacy-preserving data analysis across institutions. Technologies such as Federated Learning and Edge Computing allow the development of high-performing AI models without centralized data storage, addressing regulatory constraints while opening new pathways for innovation. However, the adoption of distributed AI introduces multidimensional tensions involving technological, economic, legal, and ethical domains. This paper proposes an integrated analytical framework to assess and navigate these tensions in the context of healthcare governance. The framework encompasses four key axes: (1) Technology, focusing on interoperability, resilience, and performance; (2) Economy, addressing cost-efficiency, value distribution, and sustainability; (3) Law, analyzing compliance with GDPR, AI Act, and sector-specific regulations; and (4) Ethics, highlighting fairness, transparency, and patient autonomy. Each axis is detailed through specific constructs and evaluation metrics. We apply this model to real-world healthcare scenarios, identifying critical trade-offs, such as those between security and cost, innovation and regulation, or algorithmic performance and interpretability. The resulting tension matrix offers a tool to visualize interdependencies and prioritize governance actions. By integrating interdisciplinary expertise, the proposed framework supports adaptive governance strategies tailored to the dynamic nature of AI systems and healthcare environments. Our approach facilitates responsible innovation by aligning technological capabilities with legal requirements, ethical principles, and economic viability. The paper concludes by highlighting future research directions and policy implications for sustainable AI adoption in digital health.