How clinical machine learning teams must track, hash, and version clinical training datasets to survive FDA regulatory inspections.
During an FDA premarket inspection, auditors demand exact proof of the datasets used to train and validate medical algorithms: which hospital supplied the scans, how ground truth was adjudicated by radiologists, and whether patient data was properly partitioned to prevent data leakage.
We explain how to pair Git with Data Version Control (DVC): storing lightweight cryptographic hashes of multi-terabyte imaging datasets in git repositories while storing binary data in immutable cloud object storage, ensuring absolute reproducibility.
Best practices for recording inter-rater variability when multiple medical experts disagree on pathological boundaries, preserving raw reader annotations in the audit trail.