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 devic e(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" .
Why Should You Attend:
The US FDA has announced 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. 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 authorized an artificial intelligence based device for detecting diabetic retinopathy, an eye disease that can cause vision loss.
The Agency has also authorized a second artificial intelligence based device for alerting providers of a potential stroke in patients. The authorization of these technologies was 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. AI software validation has some new requirements as well. 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 Webinar:
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 anticipated by the US FDA. This information applies to personnel / companies in the Medical Device and some Combination fields. The employees who will benefit include:
All personnel involved in a U.S. FDA-regulated environment. Especially those involved in new product development with products and productioin / test equipment that are software driven, companies with quality management / MRP / ERP systems moving toward "paperless" systems, or other regulated medical products / CGMP and ISO QMS activities.Instructor Profile:
John E. Lincoln, is Principal of J. E. Lincoln and Associates LLC, a consulting company with over 41 years experience in U.S. FDA-regulated industries, 27 years as head of his own consulting company. John has worked with companies from start-up to Fortune 100, in the U.S., Mexico, Canada, France, Germany, Sweden, China and Taiwan. He specializes in quality assurance, regulatory affairs, QMS problem remediation and FDA responses, new / changed product 510(k)s, process / product / equipment including QMS and software validations, ISO 14971 product risk management files / reports, Design Control / Design History Files, Technical Files. He's held positions in Manufacturing Engineering, QA, QAE, Regulatory Affairs, to the level of Director and VP (R&D). In addition, John has prior experience in military, government, electronics, and aerospace. He has published numerous articles in peer reviewed journals, 5 chapters in the RAPS textbook on validation, conducted workshops and webinars worldwide on CGMP subjects. John is a graduate of UCLA.
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