FDA regulation of Artificial Intelligence/ Machine Learning programs

Overview:
AI/ ML will revolutionize medicine by making diagnosis and treatment more accessible and more effective. FDA has regulated medical software by means of regulation and guidance for years, however, AI/ML programs fall outside the scope of these regulations and guidance. This happens because the FDA approves the final, validated version of the software. The point of AI/ML is to learn and update the following deployment to improve performance. Thus the field version of the software is no longer the validated approved version.

This training will address the current regulatory requirements, how they don’t control AI/ML adequately, and approaches FDA is considering for regulation in the near future. Development personnel should understand these concepts because, with some modifications, they will probably become regulations.
It is not clear how to get AI/ML programs approved. Following the discussion of possible future regulation, we will discuss, based on recently approved De Novo applications, how to get your AI/ML program approved now.

Areas Covered in the Session:
•  Total product life cycle approach to AI/ ML design
•  Application of FDA software Pre Cert program to AI/ ML
•  FDA discussion paper on AI/ML
•  Database management
•  QC of datasets
•  Algorithm updating
•  Reference standard development
•  Standalone performance testing
•  Clinical performance testing
•  Data enrichment
•  Emphasis on “explainability”
•  Additional labeling requirements
•  Cybersecurity

Speaker: Edwin Waldbusser
Edwin Waldbusser is a consultant retired from industry after 20 years in management of the development of medical devices (5 patents). He has been consulting in the US and internationally in the areas of design control, risk analysis, and software validation for the past 11 years. Mr. Waldbusser has a BS in Mechanical Engineering and an MBA. He is a Lloyds of London certified ISO 9000 Lead Auditor and a member of the Thomson Reuters Expert Witness network.

Tags: AI, Algorithm updating, Artificial Intelligence, Clinical performance testing, Cybersecurity, Data enrichment, FDA Compliance, FDA regulation of Artificial Intelligence, FDA regulation of Machine Learning, FDA Regulations, labeling requirements, Latest Trends in FDA, Machine Learning, ML, product life cycle approach to AI/ ML design, QC of datasets, Reference standard development, regulatory requirements for Artificial Intelligence, regulatory requirements for Machine Learning, Standalone performance testing

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