The main purpose of this new guidance t is to create uniform expectations and understanding for Machine Learning-enabled Medical Devices (MLMD), improve patient safety, inspire innovation, and encourage access to breakthroughs in healthcare technology.
Artificial Intelligence (#ai ) is an area that employs algorithms or models to accomplish tasks and display characteristics such as learning, making decisions and generating predictions. In the subset of AI known as Machine Learning (#ml), models may be built via ML training algorithms without explicitly programming them.
There are several examples in the #mlhealthcare field, such as:
- early illness detection and diagnosis,
- novel discoveries or patterns in human physiology,
- the creation of individualised diagnostic tests and therapies,
- workflow optimization, and
- signal processing and reconstruction.
There has been increased uptake and application of ML-enabled techniques in medical devices. We refer to these medical devices as MLMD. Medical devices and software as a medical device (SaaS) are the most common delivery methods for AI-based solutions. New and essential insights may be gleaned from the large amounts of data that are created during all stages of the healthcare process using MLMD. One of the main benefits of MLMD lay in the ability for continued learning and iteration when new data becomes available, including from real-world use and experience to improve its performance.
