Machine Learning for Medical Devices
Machine Learning (ML) and Artificial Intelligence are being applied to many Medical Devices. Sunrise Labs discusses what makes a good ML device application, as well as common pitfalls and techniques to overcome them, illustrated by real-life examples.
- When to consider ML for a medical product
- Understanding ML results
- Lessons and what to watch out for
, Chief Technology Officer, Sunrise Labs
Adam is a senior level technology leader with proven success in R&D, medical device development, and product innovation. Prior to joining Sunrise, he was Executive Director of Engineering at KMC Systems and Vice President R&D for Datascope, Melasciences and JADS Technologies. Adam brought three startup companies to successful liquidity events. He has developed many novel technologies from conception through product throughout his career. Adam has broad experience at a systems level with the technical acumen to solve difficult problems in a broad range of fields, bridging the elements in complex R&D projects with business, operational and user needs. Adam holds a Bachelor of Science in Mechanical Engineering from the University of California, Santa Barbara, and studied graduate Computer Science at Stanford University. He was Guest Lecturer at the Columbia University School of Engineering and Applied Science. He holds 13 US and other international patents and was awarded the NY Intellectual Property Law Association Inventor of the Year Award.
Bob Bouthillier, Senior Director & Technical PM, Sunrise Labs Southern New england Office
Bob has more than 30 years’ experience in roles of increasing responsibility as an Electronic Engineer and Executive. Bob founded Design Net Technical Products in 1995 where he has developed technology-oriented products that include medical instruments, surgical vision systems, surgical lasers, patient monitoring systems, and wireless wearable medical and consumer products. He has led interdisciplinary teams with disciplines including Mechanical Design, Electronics, Embedded Software and Machine Learning as the Director of Engineering for Jabil’s Radius Innovation Division. Bob studied Electrical Engineering and received his Bachelor’s degree from the University of RI and has continued his formal education in Machine Learning and AI system development through the Stanford University series taught by Andrew Ng.
For more than a quarter of a century, MassMEDIC has been the voice of the groundbreaking medical technology industry in New England, advocating for sound public policy that supports innovation, and fostering a community built on a shared purpose: Saving and improving the lives of patients everywhere through medical technology.
Since the beginnings of this region’s dominance in health technology, our members – the women and men who make up the largest regional medtech association in the United States – are researching, designing, developing, supplying, manufacturing, and deploying the next generation of medical innovation around the globe.