Compliance-by-Design in an AI-Enabled Platform for Early Crohn Disease Interception: A Viewpoint.

Abstract

AI-enabled digital medicine platforms in Europe are developed within overlapping rules for personal data, medical devices, AI, clinical research, health data access, and health technology assessment. This viewpoint represents the authors' experience and interpretation from the preimplementation phase of INTERCEPT, an Innovative Health Initiative consortium that plans to validate biomarkers and develop a blood-based risk score for early Crohn disease interception. It does not report original empirical research, a systematic regulatory review, a deployed platform, or a validated AI model. Our aim is to explain how compliance-by-design can be translated into an auditable governance architecture and to identify what still requires scientific evaluation. We advance 3 messages. First, governance must begin with intended purpose and boundary mapping: a digital platform may contain research, operational, and medical device software functions, and conformity of one module cannot be inferred for the platform as a whole. Second, overlapping obligations can be operationalized through 5 linked controls: modular responsibility and change control, federated data governance, traceable AI life cycle management, consent and participant rights orchestration, and a common cross-border governance core with local adaptations. Third, compliance is not a surrogate for model validity, clinical benefit, trust, scalability, or market access. Before clinical deployment, planned AI components require prespecified labels and cohorts, leakage prevention, missing data and imbalance strategies, calibration, subgroup assessment, external validation, human factors evaluation, and postdeployment algorithmovigilance. A hypothetical biomarker risk score update illustrates how the 5 controls interact. INTERCEPT is informative because it combines cross-border biomarker research, planned predictive analytics, and heterogeneous institutional settings while key design decisions remain modifiable. Its contribution is nevertheless conceptual: the proposed framework is a testable governance hypothesis, not evidence of implementation success. Prospective studies should compare it with alternative governance models and assess safety, workload, participant experience, evidence generation, and regulatory performance.