Europe – Machine Learning-enabled Medical Devices: Key Terms and Definitions

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…