Regulatory-Compliant Infrastructure for AI Validation and Lifecycle Management in Healthcare

Enes Hosgor

Keywords: AI validation, lifecycle management, regulatory compliance, Good Machine Learning Practice, dataset integration, continuous monitoring, audit trails, federated learning, data governance, multi-stakeholder consortium

Abstract: This paper presents a robust, regulatory-compliant infrastructure specifically developed to address the validation and lifecycle management of artificial intelligence (AI) applications in healthcare. This infrastructure enables rigorous validation, seamless integration, and continuous monitoring of AI-driven healthcare solutions in alignment with established regulatory guide-lines. By emphasizing transparency, reproducibility, and interoperability, the proposed infrastructure facilitates trust and adoption among stakeholders. Key components include curated public and proprietary datasets, standardized validation workflows, structured Data Use Agreements (DUAs), com-prehensive version control, defined access rights, data sequestration proto-cols, traceability, audit trails, and anti-competitive safeguards within a multi-stakeholder consortium comprising data providers, data users, model providers, model users, and technology and service providers.

Submission Number: 6