MLOps for Medical Devices
The set of practices and tooling for deploying, monitoring, retraining, and governing machine learning models in regulated medical device software environments.
Definition
MLOps for medical devices is the discipline of operating AI/ML models in production under medical device quality system controls. It extends generic MLOps (model versioning, CI/CD pipelines, monitoring, feature stores, experiment tracking) with the additional requirements of IEC 62304 (software lifecycle), ISO 13485 (QMS), 21 CFR Part 820/QMSR (design controls), AAMI CR34971 (AI risk management), and the change-control discipline of a PCCP/ACP. Core MLOps capabilities for medical devices include validated training pipelines, immutable model registries, drift monitoring with patient-safety thresholds, controlled rollback, and audit trails tying every production prediction back to a versioned model and its qualification evidence.What this means in practice
FDA-cleared AI/ML devices that include a PCCP rely on MLOps infrastructure to make iterative updates safe and traceable. The most common gap is connecting MLOps tooling to the quality system, many ML teams have excellent versioning and monitoring but cannot demonstrate the traceability and document control regulators expect. Mature MLOps for medical devices unifies the data science workflow with the design history file rather than maintaining them as parallel universes.- •Maintaining ML pipelines outside the QMS, making production changes without design control evidence creates inspection findings.
- •Monitoring model performance with engineering metrics only (accuracy drift) without patient-safety thresholds, drift alone isn't actionable.
- •Treating MLOps as a generalist DevOps task, the data lineage, model lineage, and dataset versioning requirements are materially stricter than for non-ML software.
Related terms
Grouped by themeEditor's picks
· Hand-selected related conceptsMedical device that uses artificial intelligence or machine learning to perform its intended use.
The detailed procedural section of a PCCP that specifies how planned modifications to an AI/ML model will be developed, validated, and implemented.
Structured documentation describing a dataset's motivation, composition, collection process, preprocessing, uses, and maintenance, the dataset equivalent of a model card.
Guiding principles for the development of AI/ML-enabled medical devices.
More in Software & AI
· Same categoryDistinction between models whose behavior is fixed at release vs. those that continue learning.
Structured documentation of a machine learning model's intended use, performance, and limitations.
Ongoing surveillance of input data and model outputs to detect performance degradation post-deployment.
FDA mechanism to pre-authorize specific modifications to AI/ML-enabled devices.
Primary references
3 sources- 1
PCCP Guidance for ML-Enabled DevicesVerifiedFDAfda.gov
- 2
FDA - Software as a Medical Device (SaMD)VerifiedFDAfda.gov
- 3
FDA - AI/ML-Enabled Medical DevicesVerifiedFDAfda.gov
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