Data Governance (AI/ML)
Policies, controls, and documentation for the data used to train, tune, and validate medical AI models.
Definition
Covers data provenance, consent, de-identification, demographic representativeness, labeling quality, version control, and audit trails. EU AI Act Article 10 codifies many of these as legal requirements for high-risk systems. Good practice includes documenting where each dataset came from, the inclusion and exclusion criteria, how labels were created and checked, and how datasets were split so test data stays independent. Article 10 of the EU AI Act requires training, validation and test data to be relevant, sufficiently representative and, to the best extent possible, free of errors. Privacy rules such as HIPAA and the GDPR also apply to how health data is used.What this means in practice
Weak training-data governance is the most common reason medical AI submissions stall; reviewers want to see lineage as clearly as a DHF traces design.Examples
- A medical device company implements a data governance framework that includes automated tracking of all data inputs, preprocessing steps, and feature engineering for its AI-powered diagnostic algorithm, providing a clear audit trail for FDA submission.
- Before deploying an AI model for cardiac analysis, a manufacturer performs a thorough demographic analysis of its training dataset to ensure it includes sufficient representation from various age groups, genders, and ethnic backgrounds, addressing potential biases.
- During the development of an AI-driven imaging system, the development team meticulously documents the process of de-identifying patient data to comply with HIPAA regulations, ensuring patient privacy while enabling model training.
- •Failing to document the complete lineage of training data, from raw acquisition to final processing, can lead to submission delays or rejections.
- •Using biased or unrepresentative datasets without mitigation strategies can result in AI models that perform poorly or inequitably across different patient populations, raising ethical and regulatory concerns.
- •Inadequate data labeling processes can introduce errors that propagate through the AI model, compromising its accuracy and reliability.
- •Neglecting to implement robust version control for datasets makes it difficult to reproduce model training or trace changes, hindering regulatory compliance.
- •Overlooking cybersecurity measures for data used in AI/ML development and deployment can expose sensitive patient information to breaches, violating privacy regulations like GDPR and HIPAA.
Frequently asked questions
Related terms
Grouped by themeEditor's picks
· Hand-selected related conceptsSystematic differences in model performance across demographic or clinical subgroups.
EU regulation establishing risk-based requirements for AI systems, including most medical AI.
Guiding principles for the development of AI/ML-enabled medical devices.
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Resilience of an ML model to inputs deliberately crafted to cause misclassification.
Medical device that uses artificial intelligence or machine learning to perform its intended use.
Historical FDA AI/ML change-control terminology, now reflected in the PCCP Modification Protocol.
Where this term appears across MedTech Terms.
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Sources
3 sourcesSources support the definition or relevant regulatory context, not necessarily every claim on this page. Each is graded against our source-tier hierarchy so you can see what rests on binding law versus commentary.
- 1EU AI Act Article 10Tier 1 VerifiedEuropean Parliamentartificialintelligenceact.eu
- 2FDA - AI/ML-Enabled Medical DevicesTier 2 VerifiedFDAfda.gov
- 3IMDRF - Software as a Medical DeviceTier 2 VerifiedIMDRFimdrf.org
Inline markers like [1] jump to the matching reference above.