IEEE 2801-2022

This recommended practice identifies best practices for establishing a quality management system for data sets used for artificial intelligence in medical devices. It covers a full cycle of data set management, including items, such as but not limited to, data collection, transfer, utilization, storage, maintenance, and updates. The recommended practice recommends a list of critical factors that impact the quality of data sets, such as but not limited to, data sources, data quality, annotation, privacy protection, personnel qualification/training/ evaluation, tools, equipment, environment, process control, and documentation.

The purpose of this recommended practice is to establish rules of quality management of data sets for medical artificial intelligence and improve overall data quality.

New IEEE Standard - Active. Promoted in this recommended practice are quality management activities for datasets used for artificial intelligence medical devices (AIMD). The document highlights quality objectives for organizations responsible for datasets. The document describes control of records during the lifecycle of datasets, including but not limited to data collection, annotation, transfer, utilization, storage, maintenance, updates, retirement, and other activities. The document emphasizes special consideration for the dataset quality management system, including but not limited to responsibility management, resource management, dataset realization, and quality control.