FDA Regulation of Artificial Intelligence / Machine Learning
- Product Id : FDB3397
- Category : Clinical & Laboratory, FDA Compliance, Food, Drugs & Biologics, Information Technology, Medical Devices, Quality
- Presenter : Edwin Waldbusser
- Scheduled On : September 03 2020 1:00 pm
- Duration : 60 Minutes
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’s for years, however, AI/ML programs fall outside the scope of these regulations and guidance’s. This happens because FDA approves the final, validated version of the software. The point of AI/ML is to learn and update following deployment to improve performance. Thus the field version of the software is no longer the validated approved version.
We will discuss the current regulatory requirements, how they don’t control AI/ML adequately, and approaches FDA is considering for regulation in the near future. Your development program should conform to these concepts now because, with some modifications, they will probably become regulations.
Following discussion of possible future regulation, we will discuss, based on recently approved De Novo applications, how to get your AI/ML program approved now. Necessary submission documentation will be explained.
This webinar is not a programming course but will explain the present and future regulatory requirements for AI/ML.
Why You Should Attend:
It is not clear how to get AI/ML programs approved. The current regulatory requirements don’t control AI/ML adequately. We will discuss the approaches FDA is considering for regulation in the near future and how to get your AI/ML program approved by FDA now. Necessary submission documentation will be explained.
Attendees will receive a multipage outline and checklist.
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 labelling requirements
Who Should Attend:
- Quality Assurance Departments
- Research and Development Departments
- Regulatory Affairs Departments
- Engineering Departments
- Software Engineers
- Marketing Departments
- IT Departments
- Management Teams