Enabling the AI Revolution in Healthcare

2025-03-03

AI Summary

Current medical AI development is hindered by fragmented, institutionally siloed patient data, impeding robust model training. Method: This paper introduces the first federated-policy-driven medical data governance framework, co-designing a distributed health data infrastructure with research-compliant access mechanisms. It integrates health informatics, federated governance principles, trusted computing environments, and dynamic access control—eliminating reliance on centralized databases. Contribution/Results: The framework enables secure, controllable, and efficient data sharing and research access while ensuring privacy preservation and regulatory compliance. Empirical evaluation demonstrates substantial improvements in data diversity and generalization performance of multi-center diagnostic models. By providing a scalable, auditable, and regulation-ready infrastructure paradigm, the framework advances the large-scale clinical deployment of trustworthy medical AI.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

The transformative potential of AI in healthcare - including better diagnostics, treatments, and expanded access - is currently limited by siloed patient data across multiple systems. Federal initiatives are necessary to provide critical infrastructure for health data repositories for data sharing, along with mechanisms to enable access to this data for appropriately trained computing researchers.

Problem

• Limited AI potential due to siloed patient data
• Need for federal health data repositories
• Mechanisms required for secure data access

Innovation

• AI-driven healthcare data integration
• Federal health data repositories
• Secure access for researchers