AI in Software as a Medical Device (SaMD)
Instructor: Live: One Dial-in One Attendee
Corporate Live: Any number of participants
Recorded: Access recorded version, only for one participant unlimited viewing for 6 months ( Access information will be emailed 24 hours after the completion of live webinar)
Corporate Recorded: Access recorded version, Any number of participants unlimited viewing for 6 months ( Access information will be emailed 24 hours after the completion of live webinar)
The artificial intelligence technologies granted FDA marketing authorization and cleared by the Agencyhave mostly been "locked" algorithms that don’t continually adapt or learn every time the algorithm is used.
Now 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 now regulating 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), without requiring frequent updates or submissions.
This webinar will discuss information specific to devices that include artificial intelligence algorithms that make real-world modifications that the Agency would 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" (PCCP).
Why you should Attend:
The US FDA has announced a new regulatory framework specifically tailored to promote the development of safe and effective medical devices that use advanced artificial intelligence / machine learning algorithms / software / firmware. 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 FDA recognizes that medical devices will incorporate advanced artificial intelligence algorithms to improve their performance and safety. Since AI changes over time, how will the Agency control these changes without requiring new marketing submissions. AI software validation has some new requirements as well. The FDA is continually developing new ways to keep up with the rapid pace of innovation and ensure the safety of such AI-enabled devices, requiring adjustments in the way companies meet these requirements in their V&V, documentation and post-market monitoring.
Areas Covered in Session:
- The FDA's AI "Framework" for AI in Software as a Medical Device (SaMD)
- Roles of V & V
- IEC 62304; FDA's Similar Software Guidance
- FDA AI device submission requirements
- A Typical Software V&V Protocol / Test Report; "Black" and "White" box
- "Locked" vs ML algorithms
- Predetermined Change Control in AI
- Regulatory Submission Deliverables
Who Will Benefit: This webinar will provide valuable assistance to all regulated companies in evaluating their existing or anticipated AI medical device R&D and production activities and documentation requirements, as currently required by the US FDA. This information applies to personnel / companies in the Medical Device and some Combination products fields. The employees who will benefit include:
- Software Engineering
- Senior Management
- Regulatory Affairs
- Quality Assurance / QAE
- Production
- Engineering, R&D, and Software Development and Testing Teams
All personnel involved in a U.S. FDA-regulated environment. Especially those involved in new product development with products that are software driven, and the resultant documentation and submission requirements.
John has also managed pilot production, regulatory affairs, product development/design control, 510(k) submissions, risk management per ISO 14971, and projects; with over 28 years of experience in the FDA-regulated medical products industry - working with start-ups to Fortune 100 companies, including Abbott Laboratories, Hospira, Tyco/Mallinckrodt. He is a graduate of UCLA.