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Recorded Webinar: Machine Learning in Medical Devices

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Medical device manufactures are always looking for ways to improve their products, while keeping an eye on FDA guidance. But what happens when device manufacturers incorporate forward-leaning technology in the development of safety-critical medical devices? Or when some algorithm development happens outside of the device? What if software comes with unintended outcomes? In all these scenarios, the system must keep the patients and users safe by employing safeguards.

As an example of the risks inherent in innovation, you can look at the case of using Artificial Intelligence / Machine Learning (AI/ML) as a way of optimizing a patient’s therapy or diagnosis. To improve the therapy selection or to make a diagnosis more accurate, you can train software with an aggregation of thousands of datasets, cross-sectioned by different demographics. While it may improve outcomes, using ML in this way can introduce challenges that make it hard to comply with HIPAA rules.

While the benefits are clear, the means of implementing ML are just now being defined. Recent guidance for ML in medical devices defines a strategy to manage self-modifying software. By making use of trusted, pre-certified components, medical device manufacturers can minimize the impact of new algorithms on software training used in ML model creation and on the software component lifecycle.

This webinar will outline the key strategies and illustrate how manufacturers will pursue these advanced technologies, while balancing the needs of safety with this new paradigm.

Key takeaways will include:

• How to make use of AI or ML in a medical device
• Strategies to update a medical device in the field
• Key safeguards to protect both patients and medical professionals
• Total software lifecycle management


Milton Yarberry, Director of Medical Programs, ICS

Milton Yarberry is the Director of Medical Programs at Integrated Computer Solutions, Inc (ICS). He is a certified PMP with a background in software architecture, medical device product development and program management. He spent a decade in consulting working with startup companies, and 15 years working with Class II and Class III medical device manufacturers.

Stephen Olsen, Senior Manager of Field Application Engineering, BlackBerry QNX

Stephen Olsen is a noted embedded industry expert with extensive experience in embedded software development, thought leadership, product management, and communications. He is currently a Senior Manager of Field Application Engineering with BlackBerry QNX. Prior to QNX, Stephen worked with several other real time operating system vendors in many roles, including product line manager, consultant, system architect, engineering manager and technical marketing.