Risk Management for AI in Medical Devices: Insights from FDA's Lifecycle Management Draft Guidance

The artificial intelligence technologies granted FDA marketing authorization and cleared by the agency so far are generally called “locked” algorithms that don’t continually adapt or learn every time the algorithm is used.

However, the FDA is looking beyond these elemental devices, to those capable of true AI, - machine learning algorithms that continually evolve, often called “adaptive” or “continuously learning” algorithms. Adaptive algorithms can learn from new user data presented to the algorithm through real-world use. The FDA is exploring a framework to allow modifications to algorithms to be made from real-world learning and adaptation, while still ensuring safety and effectiveness of the software as a medical device (SaMD) is maintained.

This webinar will discuss information specific to devices that include artificial intelligence algorithms that make real-world modifications that the agency might require for premarket review. They include the algorithm’s performance, the added concerns for AI / ML software verification and Validation, the manufacturer’s plan for modifications and the ability of the manufacturer to manage and control risks of the modifications, including the software’s "predetermined change control plan", throughout the device's total product lifecycle.

Why should you Attend: The US FDA has announced a Draft Guidance addressing steps toward a new regulatory framework specifically tailored to promote the development of safe and effective medical devices that use advanced artificial intelligence / machine learning algorithms, throughout their lifecycle. Artificial intelligence algorithms are software that can learn from and act on data.

These types of algorithms are already being used to aid in screening for diseases and to provide treatment recommendations. The recent authorization of devices using these technologies is a harbinger of progress that the FDA expects to see as more medical devices incorporate advanced artificial intelligence algorithms to improve their performance and safety throughout the TPLC (Total Product Lifecycle) of these devices. The Agency plans to apply their current authorities in new ways to keep up with the rapid pace of innovation and ensure the safety of these devices.

Areas Covered in the Session:

• Roles of Verification and Validation
• The FDA's AI TPLC Management Draft Guidance
• FDA AI device submission requirements
• A Typical Software V&V Protocol / Test Report; "Black" and "White" box
• Predetermined Change Control in AI
• Expected Regulatory Submission Deliverables
• The Future of AI in Medical Devices

• Software Engineering
• Senior Management
• Regulatory Affairs
• Quality Assurance / QAE
• Production
• Engineering
• R&D
• Software Development and Testing Teams