Transforming oncology registries into scalable real-world evidence platforms through a governance and interoperability roadmap for AI-ready clinical integration

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Abstract
Purpose: Real-world evidence (RWE) and real-world data (RWD) have become integral to regulatory, clinical, and policy decision-making. Yet oncology registries—the most structured and disease-specific RWD sources—remain fragmented, inconsistently governed, and poorly integrated with AI-driven analytical systems. This conceptual perspective synthesizes regulatory guidance, best practices, and three decades of professional experience to propose a pragmatic roadmap for transforming oncology registries into scalable, interoperable RWE platforms. Methods: The approach integrates practice-based evidence, international regulatory frameworks (EMA, FDA, MHRA), and case analyses from established registries and digital infrastructures. A three-year phased roadmap is proposed, focusing on governance, interoperability, artificial intelligence (AI) readiness, and multi-stakeholder collaboration. Although not a systematic review, the manuscript draws on applied use-cases and policy frameworks to illustrate transferability across healthcare systems. Results: Three conceptual models are developed to describe: (1) the main RWD domains; (2) the bidirectional alignment between RWE and RWD strategies; and (3) a phased roadmap for data activation, hybrid platform development, and AI-ready evidence generation. Case studies from the UK, France, Italy, Germany, and Finland demonstrate how registry modernization can enhance completeness, quality, and regulatory utility. Conclusions: Oncology registries can evolve from static repositories into dynamic learning infrastructures that generate regulatory-grade RWE. Achieving this transformation requires transparent governance, federated data architectures, Findable, Accessible, Interoperable, and Reusable (FAIR)-compliant interoperability, and ethically grounded AI integration. The proposed roadmap offers actionable guidance for regulators, payers, clinicians, and data scientists committed to advancing trustworthy, AI-enabled evidence ecosystems.

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