Artificial Intelligence (AI) is a branch of computer science, statistics, and engineering that uses algorithms or models to perform tasks and exhibit behaviors such as learning,making decisions and making predictions. The subset of AI known as Machine Learning (ML) allows ML models to be developed by ML training algorithms through analysis of data, without models being explicitly programmed.

Approaches utilizing ML, sometimes colloquially referred to as AI or AI/ML, have been employed in several fields, such as the automotive industry, robotics, medicine, finance, and art. ML has given many sectors an ability to gain new insights from large amounts of data and to support tasks.

Examples in healthcare applications include earlier disease detection and diagnosis; identification of new observations or patterns on human physiology; development of personalized diagnostics and therapeutics; workflow optimization; signal processing and reconstruction; and guidance in use of the device with the goal of improving user and patient experience. There has been accelerated adoption and use of ML-enabled approaches in medical devices. We refer to these medical devices as Machine Learning-enabled Medical Devices, or MLMD. AI-based systems are typically implemented as software in medical devices or as Software as a Medical Device. MLMD have the potential to transform health care by deriving new and important insights from the vast amount of data generated during all phases of the healthcare process. One of the greatest benefits of MLMD resides in the opportunity for further learning and iteration as additional data becomes available, including from real-world use and experience to improve its performance.

The purpose of this publication is to establish relevant terms and definitions across the Total Product Life Cycle (TPLC) to promote consistency, support global harmonization efforts, and provide a foundation for the development of future guidelines related to MLMD. Terms referenced herein have either been previously defined in Global Harmonization Task Force (GHTF) documents or by internationally recognized standards on AI. Some terms and definitions have been generated by or are discussed by the IMDRF Artificial Intelligence Medical Devices (AIMD) Working Group within this document.

The overarching objective of this effort is to promote consistent expectations and understanding for MLMD, promote patient safety, foster innovation, and encourage access to advances in healthcare technology.


IMDRF News : Machine Learning-enabled Medical Devices: Key Terms and Definitions