Instructor :
Carolyn Troiano

Webinar ID:
13565

The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) is transforming the life sciences industry, making compliance with FDA regulations, 21 CFR Part 11, and data integrity more critical than ever.

Organizations must ensure that their computer systems, electronic records, and AI-driven processes meet regulatory expectations to avoid costly non-compliance risks.

In recent years, the FDA has intensified its scrutiny, issuing a significant number of Form 483s and Warning Letters for violations related to data integrity and electronic records management.

The challenge? Many organizations struggle to keep up with these evolving regulations while balancing increased workloads, tighter deadlines, and limited resources.

This comprehensive training will provide a deep dive into:

• • Key FDA Compliance Requirements
    • • Understanding 21 CFR Part 11, Computer System Validation (CSV), and data integrity best practices.

• • AI and Machine Learning in Compliance
    • • How AI-driven tools can be leveraged while maintaining regulatory compliance and validation standards.

• • Common Compliance Pitfalls & Risk Management
    • • Identifying and mitigating compliance vulnerabilities in AI and automated systems.

• • Regulatory Trends & Industry Best Practices
    • • Insights into FDA expectations, warning trends, and audit preparedness for AI-integrated systems.

With AI revolutionizing software development, testing, and operations in regulated environments, this course equips professionals with the knowledge and tools to navigate compliance challenges while ensuring innovation, efficiency, and safety in life sciences.

Enroll Now!

The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) is transforming the life sciences industry, making compliance with FDA regulations, 21 CFR Part 11, and data integrity more critical than ever.

Organizations must ensure that their computer systems, electronic records, and AI-driven processes meet regulatory expectations to avoid costly non-compliance risks.

In recent years, the FDA has intensified its scrutiny, issuing a significant number of Form 483s and Warning Letters for violations related to data integrity and electronic records management.

The challenge? Many organizations struggle to keep up with these evolving regulations while balancing increased workloads, tighter deadlines, and limited resources.

This comprehensive training will provide a deep dive into:

• • Key FDA Compliance Requirements
    • • Understanding 21 CFR Part 11, Computer System Validation (CSV), and data integrity best practices.

• • AI and Machine Learning in Compliance
    • • How AI-driven tools can be leveraged while maintaining regulatory compliance and validation standards.

• • Common Compliance Pitfalls & Risk Management
    • • Identifying and mitigating compliance vulnerabilities in AI and automated systems.

• • Regulatory Trends & Industry Best Practices
    • • Insights into FDA expectations, warning trends, and audit preparedness for AI-integrated systems.

With AI revolutionizing software development, testing, and operations in regulated environments, this course equips professionals with the knowledge and tools to navigate compliance challenges while ensuring innovation, efficiency, and safety in life sciences.

Enroll Now!

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in FDA-regulated systems presents both opportunities and challenges.

While AI can significantly enhance efficiency, it also introduces compliance risks, cybersecurity threats, and regulatory complexities that organizations must navigate carefully.

If you work in pharmaceuticals, medical devices, biologics, or any FDA-regulated sector, staying ahead of evolving regulations is no longer optional—it is essential.

This training will provide:

• • Practical Strategies for AI Compliance
    • • Learn how to integrate AI and ML within the Software Development Life Cycle (SDLC) while meeting FDA expectations.

• • Cybersecurity Risk Mitigation
    • • Understand the growing threat of cyberattacks on medical devices and software applications and how to safeguard against them.

• • Regulatory Best Practices
    • • Gain insights into current FDA guidelines, industry trends, and future compliance expectations for AI-driven technologies.

• • Real-World Case Studies
    • • Explore successful implementations and lessons learned from industry leaders to ensure your organization stays compliant and competitive.

If you are responsible for developing, testing, validating, implementing, or maintaining AI-driven systems in life sciences, this course is designed to equip you with the knowledge and tools needed to navigate regulatory challenges with confidence.

Join Now!

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in FDA-regulated systems presents both opportunities and challenges.

While AI can significantly enhance efficiency, it also introduces compliance risks, cybersecurity threats, and regulatory complexities that organizations must navigate carefully.

If you work in pharmaceuticals, medical devices, biologics, or any FDA-regulated sector, staying ahead of evolving regulations is no longer optional—it is essential.

This training will provide:

• • Practical Strategies for AI Compliance
    • • Learn how to integrate AI and ML within the Software Development Life Cycle (SDLC) while meeting FDA expectations.

• • Cybersecurity Risk Mitigation
    • • Understand the growing threat of cyberattacks on medical devices and software applications and how to safeguard against them.

• • Regulatory Best Practices
    • • Gain insights into current FDA guidelines, industry trends, and future compliance expectations for AI-driven technologies.

• • Real-World Case Studies
    • • Explore successful implementations and lessons learned from industry leaders to ensure your organization stays compliant and competitive.

If you are responsible for developing, testing, validating, implementing, or maintaining AI-driven systems in life sciences, this course is designed to equip you with the knowledge and tools needed to navigate regulatory challenges with confidence.

Join Now!

1. Introduction to AI and Machine Learning in Life Sciences

• • Overview of AI applications in pharmaceuticals, medical devices, and biologics
  • The evolving role of AI in healthcare and regulatory landscapes
  • Key compliance challenges organizations face when integrating AI

2. FDA Regulations and AI-Enabled Medical Devices

• • How and under what circumstances AI-driven products are regulated by the FDA
  • FDA’s evolving approach to reviewing AI-enabled medical devices
  • The impact of real-time learning and continuous evolution of AI systems on compliance
  • 21 CFR Part 11 and data integrity considerations in AI and ML applications

3. Balancing Innovation and Compliance: FDA’s Perspective

• • FDA’s plans to ensure the benefits of AI-driven products outweigh the risks
  • Best practices for validation, testing, and risk assessment of AI-based systems
  • Strategies for ensuring transparency, accountability, and audit readiness

4. Industry Collaboration and Best Practices

• • The role of technology developers, healthcare providers, and regulators in shaping AI policies
  • How Congress, the FDA, and the life sciences industry must collaborate to define AI compliance standards
  • Emerging trends in AI governance and regulatory oversight

5. Q&A Session

• • Open forum for attendees to ask questions and discuss real-world challenges

BONUS:

1. 1. PDF copy of the presentation handout for your future reference.
   2. Soft copy of the certificate of completion on request.
   3. Q&A Session with the Presenter: Get your pressing questions answered verbally, via chat or email

1. Introduction to AI and Machine Learning in Life Sciences

• • Overview of AI applications in pharmaceuticals, medical devices, and biologics
  • The evolving role of AI in healthcare and regulatory landscapes
  • Key compliance challenges organizations face when integrating AI

2. FDA Regulations and AI-Enabled Medical Devices

• • How and under what circumstances AI-driven products are regulated by the FDA
  • FDA’s evolving approach to reviewing AI-enabled medical devices
  • The impact of real-time learning and continuous evolution of AI systems on compliance
  • 21 CFR Part 11 and data integrity considerations in AI and ML applications

3. Balancing Innovation and Compliance: FDA’s Perspective

• • FDA’s plans to ensure the benefits of AI-driven products outweigh the risks
  • Best practices for validation, testing, and risk assessment of AI-based systems
  • Strategies for ensuring transparency, accountability, and audit readiness

4. Industry Collaboration and Best Practices

• • The role of technology developers, healthcare providers, and regulators in shaping AI policies
  • How Congress, the FDA, and the life sciences industry must collaborate to define AI compliance standards
  • Emerging trends in AI governance and regulatory oversight

5. Q&A Session

• • Open forum for attendees to ask questions and discuss real-world challenges

BONUS:

1. 1. PDF copy of the presentation handout for your future reference.
   2. Soft copy of the certificate of completion on request.
   3. Q&A Session with the Presenter: Get your pressing questions answered verbally, via chat or email

Everybody Benefits from Watching This. Even Better When Done as a Group!

• • Compliance And Regulatory Professionals In Life Sciences
  • AI And Machine Learning Developers In Healthcare
  • Quality Assurance And Validation Specialists In Pharma
  • IT And Cybersecurity Experts In Medical Technology
  • Research And Development Teams In FDA-Regulated Industries
  • Clinical Data Managers And Healthcare Technology Leaders
  • Software Engineers Building AI-Powered Medical Applications
  • Manufacturing And Supply Chain Professionals In Life Sciences

Everybody Benefits from Watching This. Even Better When Done as a Group!

• • Compliance And Regulatory Professionals In Life Sciences
  • AI And Machine Learning Developers In Healthcare
  • Quality Assurance And Validation Specialists In Pharma
  • IT And Cybersecurity Experts In Medical Technology
  • Research And Development Teams In FDA-Regulated Industries
  • Clinical Data Managers And Healthcare Technology Leaders
  • Software Engineers Building AI-Powered Medical Applications
  • Manufacturing And Supply Chain Professionals In Life Sciences