Summary
Establishes recommended quality-management activities for datasets used in AI-enabled medical devices, including quality objectives and controls for data collection, annotation, transfer, use, storage, maintenance, updating, retirement, records, responsibilities, resources, and quality control.
Healthcare Implications
Medical-AI developers and healthcare data partners can use the standard to structure dataset governance across the lifecycle, document responsibilities and records, and reduce safety and performance failures caused by poorly controlled training, validation, or testing data.