FDA Regulation of Artificial Intelligence/ Machine Learning

Recorded Session
60 Minutes

AI and machine learning can make diagnosis and treatment more effective and easier to access, but FDA regulation of AI/ML has not kept pace. FDA has regulated medical software through regulations and guidance for years, yet these were built for software that stays as validated.

FDA approves a final, validated version, while AI/ML is meant to learn and update after deployment, so the version in the field is no longer the one that was approved. This 60-minute webinar explains the current requirements, why they don't control AI/ML well, and the approaches FDA has been considering, which your development program should follow now.

Drawing on approved De Novo applications, it then shows how to get an AI/ML program approved and what submission documents you need. It is not a programming course.

WHY SHOULD YOU ATTEND?

It is not clear how to get an AI/ML program approved, because existing regulatory requirements don't adequately control software that keeps learning.

This session explains the approaches FDA has been considering and how to get your AI/ML program through FDA now, including the documentation a submission needs. It suits managers, directors and vice presidents in software engineering, engineering, regulatory affairs, quality assurance, marketing and management.

AREA COVERED

  • Total product life cycle approach to AI/ML design
  • Applying the FDA software Pre-Cert program to AI/ML
  • The 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

LEARNING OBJECTIVES

  • Explain why current FDA software rules do not adequately control AI/ML
  • Apply a total product life cycle approach to AI/ML design
  • Describe the FDA Pre-Cert program and discussion paper concepts for AI/ML
  • Manage databases, dataset QC, reference standards and algorithm updates
  • Plan standalone and clinical performance testing
  • Address explainability, labeling and cybersecurity requirements
  • Prepare the documentation for an AI/ML submission

WHO WILL BENEFIT?

Managers, Supervisors, Directors, and Vice-Presidents in the areas of:

  • Software Engineers
  • Engineers
  • Regulatory Personnel
  • Quality Assurance Personnel
  • Marketing
  • Management

It is not clear how to get an AI/ML program approved, because existing regulatory requirements don't adequately control software that keeps learning.

This session explains the approaches FDA has been considering and how to get your AI/ML program through FDA now, including the documentation a submission needs. It suits managers, directors and vice presidents in software engineering, engineering, regulatory affairs, quality assurance, marketing and management.

  • Total product life cycle approach to AI/ML design
  • Applying the FDA software Pre-Cert program to AI/ML
  • The 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
  • Explain why current FDA software rules do not adequately control AI/ML
  • Apply a total product life cycle approach to AI/ML design
  • Describe the FDA Pre-Cert program and discussion paper concepts for AI/ML
  • Manage databases, dataset QC, reference standards and algorithm updates
  • Plan standalone and clinical performance testing
  • Address explainability, labeling and cybersecurity requirements
  • Prepare the documentation for an AI/ML submission

Managers, Supervisors, Directors, and Vice-Presidents in the areas of:

  • Software Engineers
  • Engineers
  • Regulatory Personnel
  • Quality Assurance Personnel
  • Marketing
  • Management
Currency:
Webinar Option
Transcript (PDF Transcript of the Training)
Downloadable Recorded Session
USB

Speaker Profile

ins_img Edwin Waldbusser

Edwin Waldbusser, is a consultant retired from industry after 20 years in management of development of medical devices (5 patents). He has been consulting in the areas of design control, risk analysis and software validation for the past 8 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.

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