With the rise in Big Data and increased horsepower of computing platforms, there is a surge in the development of Machine Learning-based Medical Devices (MLMD.) As with any new technology, there are many benefits, risks, and challenges in the safe and effective development and adoption of MLMD. This training provides an overview of the technology, discusses what is different between MLMD and traditional software development, and reviews the regulatory and standards landscape – both domestic and international -- for MLMD. 

As with any new technology, there are new challenges regarding the safety and efficacy of Machine Learning when used in medical devices.  This course will highlight those differences and will provide a snapshot of the current standards and regulatory landscape on machine learning for medical devices.

Over the course of two (2) hours, the attendee’s will:

• Be able to understand the differences between traditional software development and software development for Machine Learning systems.
• Be able to understand the current standards landscape for ML systems (both inside and outside of healthcare), including an overview of the AAMI/BSI TIR 34971 regarding risk management for ML systems, and also a proposed process for bias management.
• Be able to understand the current regulatory landscape for ML systems, including the FDA, EU, China’s NMPA, and efforts underway with the IMDRF.

Software Development Managers, Quality Managers, and Regulatory Affairs Professionals.