FDA Regulation of Artificial Intelligence & Machine Learning in Healthcare
Gain Actionable Insights on Managing Continuous Algorithm Updates & Ensuring Ongoing Compliance!
Instructor :
Jose Mora
Webinar ID:
10971
Date: FEB 06, 2025 (THU)
Start Time: 10 AM PT - 11 AM PT
Duration: 1 Hr.
What you will learn
-
- Integrate AI/ML into Product Lifecycle for Compliance.
- Learn FDA’s Pre-Cert Program Application to AI/ML.
- Explore FDA’s Future Regulatory Plans for AI/ML.
- Master Database Management and Data Enrichment Techniques.
- Quality Control Measures for AI/ML Dataset …
-
- Integrate AI/ML into Product Lifecycle for Compliance.
- Learn FDA’s Pre-Cert Program Application to AI/ML.
- Explore FDA’s Future Regulatory Plans for AI/ML.
- Master Database Management and Data Enrichment Techniques.
- Quality Control Measures for AI/ML Dataset Integrity.
- Update Algorithms Post-Deployment to Maintain Performance.
- Perform Rigorous Standalone and Clinical Testing on AI/ML.
- Discuss AI/ML Explainability and Cybersecurity Measures
Course Description
- Transforming Healthcare: Discover how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing diagnosis and treatment, making healthcare more efficient and accessible.
- Regulatory Challenges: Unlike traditional medical software, AI/ML systems are dynamic, continuously learning and updating after deployment. This adaptation means that the deployed version can differ from the initially FDA-approved version, posing significant regulatory challenges.
Key Topics Covered:
- Current Regulatory Framework: An overview of how the FDA has historically regulated medical software and why AI/ML falls outside these scopes due to its evolving nature.
- Evolving FDA Strategies: Insights into the FDA’s current efforts to adapt its regulatory approaches to accommodate the continuous learning capabilities of AI/ML technologies.
- Guidance and Compliance: Upcoming guidance from the FDA and strategies for companies to align their AI/ML technologies with regulatory standards to ensure a smooth review process.
Takeaways:
- Understand the impact of AI/ML on the regulatory landscape.
- Learn about the FDA’s evolving policies and how they might affect your developments in medical AI systems.
- Best practices for preparing your AI/ML technologies for regulatory scrutiny and approval.
Join Now!
- Transforming Healthcare: Discover how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing diagnosis and treatment, making healthcare more efficient and accessible.
- Regulatory Challenges: Unlike traditional medical software, AI/ML systems are dynamic, continuously learning and updating after deployment. This adaptation means that the deployed version can differ from the initially FDA-approved version, posing significant regulatory challenges.
Key Topics Covered:
- Current Regulatory Framework: An overview of how the FDA has historically regulated medical software and why AI/ML falls outside these scopes due to its evolving nature.
- Evolving FDA Strategies: Insights into the FDA’s current efforts to adapt its regulatory approaches to accommodate the continuous learning capabilities of AI/ML technologies.
- Guidance and Compliance: Upcoming guidance from the FDA and strategies for companies to align their AI/ML technologies with regulatory standards to ensure a smooth review process.
Takeaways:
- Understand the impact of AI/ML on the regulatory landscape.
- Learn about the FDA’s evolving policies and how they might affect your developments in medical AI systems.
- Best practices for preparing your AI/ML technologies for regulatory scrutiny and approval.
Join Now!
Why you should attend
- Are you prepared for the FDA’s shift towards AI/ML in medical technology?
As AI continues to transform healthcare, the regulatory landscape is racing to keep up.
” Is your team ready to navigate these evolving regulations without falling behind? “
- Understanding Regulatory Uncertainty: Navigating the approval process for AI/ML programs can be challenging due to the inadequate control of current regulatory requirements over these dynamic technologies. This webinar sheds light on these complexities and outlines steps to enhance your compliance strategy.
Key Discussion Points:
- Current Regulatory Landscape: We will explore the current regulatory framework and its shortcomings in effectively governing AI/ML technologies, providing a foundation for understanding the gaps that need addressing.
- Future FDA Approaches: Gain insights into the approaches the FDA is considering for future regulation of AI/ML. Learn how you can align your development program with these evolving concepts to stay ahead.
- Practical Compliance Strategies: Learn from recently approved De Novo applications to understand how to successfully navigate the FDA approval process under the current regulatory climate.
- Essential Documentation: We will detail the necessary submission documentation, helping you prepare your AI/ML programs for review and approval by the FDA.
What This Webinar Is Not:
- This session is not a programming course but is focused on elucidating current and future regulatory requirements specific to AI/ML technologies in the healthcare sector.
Enroll Now!
- Are you prepared for the FDA’s shift towards AI/ML in medical technology?
As AI continues to transform healthcare, the regulatory landscape is racing to keep up.
” Is your team ready to navigate these evolving regulations without falling behind? “
- Understanding Regulatory Uncertainty: Navigating the approval process for AI/ML programs can be challenging due to the inadequate control of current regulatory requirements over these dynamic technologies. This webinar sheds light on these complexities and outlines steps to enhance your compliance strategy.
Key Discussion Points:
- Current Regulatory Landscape: We will explore the current regulatory framework and its shortcomings in effectively governing AI/ML technologies, providing a foundation for understanding the gaps that need addressing.
- Future FDA Approaches: Gain insights into the approaches the FDA is considering for future regulation of AI/ML. Learn how you can align your development program with these evolving concepts to stay ahead.
- Practical Compliance Strategies: Learn from recently approved De Novo applications to understand how to successfully navigate the FDA approval process under the current regulatory climate.
- Essential Documentation: We will detail the necessary submission documentation, helping you prepare your AI/ML programs for review and approval by the FDA.
What This Webinar Is Not:
- This session is not a programming course but is focused on elucidating current and future regulatory requirements specific to AI/ML technologies in the healthcare sector.
Enroll Now!
Course Agenda
- Introduction to Total Product Life Cycle Approach
- Understand how to integrate a total product life cycle approach into AI/ML design, setting a strong foundation for development and compliance.
- Application of FDA Software Pre-Cert Program:
- Learn how the FDA’s Pre-Cert program applies to AI/ML technologies, facilitating faster and more flexible regulatory oversight.
- FDA Discussion Paper on AI/ML:
- Explore key points from the FDA’s discussion paper that highlight regulatory perspectives and future directions for AI/ML technologies.
- Database Management and Data Enrichment:
- Examine strategies for managing databases effectively and enriching data to enhance AI/ML model training and performance.
- Quality Control of Datasets:
- Delve into the quality control measures essential for ensuring the integrity and accuracy of datasets used in AI/ML applications.
- Algorithm Updating:
- Discuss the challenges and methodologies for updating algorithms post-deployment to maintain compliance and performance.
- Development of Reference Standards:
- Understand the importance of developing robust reference standards to ensure the reliability of AI/ML applications.
- Standalone Performance Testing:
- Learn about conducting standalone performance testing to evaluate the functionality and accuracy of AI/ML models independently.
- Clinical Performance Testing:
- Cover the processes involved in clinical performance testing to validate the effectiveness and safety of AI/ML applications in real-world medical settings.
- Emphasis on “Explainability”:
- Address the critical need for explainability in AI/ML systems, ensuring that machine learning decisions are interpretable by end-users.
- Additional Labeling Requirements:
- Review the additional labeling requirements specific to AI/ML products to comply with regulatory standards and enhance user understanding.
- Cybersecurity Measures:
- Highlight the importance of incorporating cybersecurity measures to protect AI/ML systems from digital threats.
BONUS:
-
- PDF copy of the presentation handout for your future reference.
- Soft copy of the certificate of completion on request.
- Q&A Session with the Presenter: Get your pressing questions answered verbally, via chat or email.
- Introduction to Total Product Life Cycle Approach
- Understand how to integrate a total product life cycle approach into AI/ML design, setting a strong foundation for development and compliance.
- Application of FDA Software Pre-Cert Program:
- Learn how the FDA’s Pre-Cert program applies to AI/ML technologies, facilitating faster and more flexible regulatory oversight.
- FDA Discussion Paper on AI/ML:
- Explore key points from the FDA’s discussion paper that highlight regulatory perspectives and future directions for AI/ML technologies.
- Database Management and Data Enrichment:
- Examine strategies for managing databases effectively and enriching data to enhance AI/ML model training and performance.
- Quality Control of Datasets:
- Delve into the quality control measures essential for ensuring the integrity and accuracy of datasets used in AI/ML applications.
- Algorithm Updating:
- Discuss the challenges and methodologies for updating algorithms post-deployment to maintain compliance and performance.
- Development of Reference Standards:
- Understand the importance of developing robust reference standards to ensure the reliability of AI/ML applications.
- Standalone Performance Testing:
- Learn about conducting standalone performance testing to evaluate the functionality and accuracy of AI/ML models independently.
- Clinical Performance Testing:
- Cover the processes involved in clinical performance testing to validate the effectiveness and safety of AI/ML applications in real-world medical settings.
- Emphasis on “Explainability”:
- Address the critical need for explainability in AI/ML systems, ensuring that machine learning decisions are interpretable by end-users.
- Additional Labeling Requirements:
- Review the additional labeling requirements specific to AI/ML products to comply with regulatory standards and enhance user understanding.
- Cybersecurity Measures:
- Highlight the importance of incorporating cybersecurity measures to protect AI/ML systems from digital threats.
BONUS:
-
- PDF copy of the presentation handout for your future reference.
- Soft copy of the certificate of completion on request.
- Q&A Session with the Presenter: Get your pressing questions answered verbally, via chat or email.
Who is this course for
Everybody Benefits from Watching This. Even Better When Done as a Group!
- Regulatory Affairs Manager
- Clinical Data Manager
- Biomedical Engineer
- Medical Device Product Manager
- Healthcare IT Specialist
- Quality Assurance Specialist
- Compliance Officer
- Medical Software Developer
- Pharmaceutical IT Analyst
- AI/ML Research Scientist
- Healthcare Data Scientist
- Digital Health Strategist
- Health Technology Compliance Consultant
- R&D Manager in Medical Devices
- Health Informatics Specialist
Everybody Benefits from Watching This. Even Better When Done as a Group!
- Regulatory Affairs Manager
- Clinical Data Manager
- Biomedical Engineer
- Medical Device Product Manager
- Healthcare IT Specialist
- Quality Assurance Specialist
- Compliance Officer
- Medical Software Developer
- Pharmaceutical IT Analyst
- AI/ML Research Scientist
- Healthcare Data Scientist
- Digital Health Strategist
- Health Technology Compliance Consultant
- R&D Manager in Medical Devices
- Health Informatics Specialist
Instructor Profile
José Mora is a Principal Consultant specializing in Manufacturing Engineering and Quality Systems. For over 30 years he has worked in the medical device and life sciences industry specializing in manufacturing, process development, tooling, and quality systems.
Prior to working full time as a consulting partner for Atzari Consulting, José served as Director of Manufacturing Engineering at Boston Scientific and as Quality Systems Manager at Stryker Orthopedics, where he introduced process performance, problem solving, and quality system methodologies. During that time he prepared a white paper on the application of lean manufacturing methods to the creation and management of controlled documents and a template for strategic deployment.
José led the launch of manufacturing at a start-up urology products company as Director of Manufacturing for UroSurge, Inc. at the University of Iowa’s business incubator park in Coralville, IA, creating a world-class medical device manufacturing operation, with JIT, Kanban systems, visual workplace and lean manufacturing practices.
José worked for 10 years at Cordis Corporation, now a Cardinal Health company, where he led the successful tooling, process development and qualification of Cordis’ first PTA (percutaneous transluminal angioplasty) catheter. His medical device experience includes surgical instruments, PTA & PTCA dilatation and guiding catheters, plastic surgery implants and tissue expanders, urology implants and devices for the treatment of incontinence, delivery systems for brachytherapy, orthopedic implants and instruments, and vascular surgery grafts and textiles.
During his time at Cordis, José managed the Maintenance and Facilities Department, taking that operation to a level rated as “tops” by the UK Department of Health and Social Services (DHSS) during one of their intensive audits. Jose managed Manufacturing Engineering as part of the Guiding Catheter Core Team of managers, a team that took the Cordis Guiding Catheter business to lead the market, bringing it up from fourth place. By introducing world-class techniques, the Guiding Catheter design and manufacturing was completely re-engineered for robust design and tooling, under Jose’s leadership.
He was also instrumental and played a leadership role in the complete re-engineering of the Tooling Control System, including design drafting, the tool shop and technical support. Wherever he has worked, he has a track record of introducing world-class methodologies such as Kepner-Tregoe, Taguchi techniques, Theory of Constraints, Lean Manufacturing, Five S (Visual Workplace), process validation to Global Harmonization Task Force standards, and similar approaches…
