AI Governance for Medical Device Manufacturers

FDA will take a risk-based approach to specific testing needs and applicable recommendations to support marketing submissions for AI-enabled devices. A “device software function” is a software function that meets the device definition in section 201(h) of the Federal Food, Drug, and Cosmetic Act (FD&C Act).

AI-enabled devices are devices that include one or more AI-enabled Device Software Functions (AI-DSFs). This guidance are intended to assist with device development and lifecycle governance of AI-enabled devices, which should help support the safety and effectiveness of these devices. This webinar provides both 1) specific recommendations on the information and documentation to support a marketing submission for an AI-enabled device, as well as 2) recommendations for the design, development, deployment, maintenance of AI devices, including performance management.

This also includes FDA’s current thinking on strategies to address transparency and bias throughout the TPLC of AI-enabled devices. These should be incorporated from device design through decommission to help design transparency and the control of bias into the device and ensure its safety and effectiveness. This webinar will provides recommendations on the documentation that should be included in compamy SOPs, WI 's as well as their marketing submissions (510(k)s, De Novos, PMAs, HDEs, BLAs) to meet and support FDA requirements.

Why you should Attend:
"This webinar will cover the FDA’s total product life cycle approach to AI governance and FDA’s roles and responsibilities in regulating medical devices throughout their lifecycle, and how this affects the manufacturer's approach to Medical Device AI governance. The US FDA has long promoted a Risk-Based Total Product Life Cycle (TPLC) approach to the oversight / governance of medical devices, including artificial intelligence (AI)-enabled devices. Some recent efforts include developing guiding principles for good machine learning practice (GMLP) and transparency for machine learning-enabled devices. This is to help promote safe, effective, and high-quality machine learning models. They have held a public workshop on fostering a patient-centered approach to AI-enabled devices, including discussions of device transparency for users.

This webinar will examine FDA's on ""Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations"", and how this should be understood by the device manufacturer's governance activities. This Guidance provides total lifecycle management and marketing submission recommendations consistent with a TPLC approach for AI-enabled devices. This guidance provides recommendations on the contents of marketing submissions (510(k), De Novos, PMAs) for devices that include AI-enabled device software functions including documentation and information that will support FDA’s review. "

Areas Covered in Session:

• What is Risk-Based?
• Total Product Life Cycle (TPLC) Approach to Medical Devices AI Guidance
• Specific FDA requirements for SI-DSF Submissions
• FDA Rrecommendations for the design, development, deployment, maintenance of AI devices
• Contents of marketing submissions To Meed Guidance Requirements
• Guidance Considerations for Pre- and Post-market

• Senior management in Devices, AI Technology
• Marketing
• QA
• RA
• R&D
• Engineering
• Production
• Operations
• Consultants; others tasked with IVD product projects