What The FDA's AI Device Software Guidance Signals For Your Traceability

By Sumatha Kondabolu

Managing artificial intelligence and machine learning models in Software as a Medical Device (SaMD) requires continuous oversight across the Total Product Lifecycle. As model thresholds shift and training datasets refresh, maintaining clear proof of performance changes becomes a core submission requirement. FDA's draft guidance outlines strict expectations for dataset provenance, requiring distinct separation between development and validation data, algorithmic equity evaluations, and ongoing real-world performance monitoring.

Static design history files and spreadsheets fail to catch dataset drift or track quarterly model retraining. Quality and regulatory teams must establish continuous audit trails and map model changes against evolving regulatory expectations before review teams evaluate submissions. Review the draft lifecycle recommendations to identify gaps in your AI/ML documentation.