The U.S. FDA has announced steps toward a new regulatory policy and framework tailored to promote the development of safe and effective drugs using advanced artificial intelligence and machine learning algorithms. AI algorithms — software that learns from and acts on data — are already used on a limited but growing scale to screen for diseases and provide treatment recommendations.

Recent FDA medical-device authorizations and drug-development policy statements signal that these technologies are viewed as a harbinger of progress the agency expects to see across the five basic elements of drug development: discovery and development, preclinical research, clinical research, FDA review, and post-marketing safety monitoring. AI production-software validation also carries some new requirements, and the agency plans to apply its current authorities in new ways to keep pace with innovation while ensuring drug safety.

Generative AI attempts to match or surpass human thinking across large data tasks, and FDA has issued policy statements on advanced forms of AI in pharma development — including AIRIS (Autonomous Intelligent Reinforcement Inferred Symbolism), which solves problems and creates rules as it navigates a virtual world without explicit programming for each task. While FDA specifically mentions AIRIS, its statements indicate broader thinking on AI in drug discovery and a willingness to expand generative AI across development, production, and post-market monitoring. This seminar evaluates these stated FDA policy shifts. Registration includes presentation slides, a certificate of completion, a live Q&A, and real-world examples.

Webinar takeaway:

• Generative AI
• AIRIS example
• The drug discovery / development process — 5 key steps and AI
• The U.S. FDA Commissioner’s comments
• Discovery and development; preclinical research; clinical research
• FDA review
• Post-market safety monitoring / reporting
• Patient-focused development

After this webinar, attendees will be able to answer -

• What new regulatory policy and risk-based framework is FDA developing for AI/ML in drug development?
• How is generative AI — and an example like AIRIS — expected to be used across drug discovery?
• How does AI apply across the five stages of drug development, from discovery to post-market monitoring?
• What new requirements apply to AI production-software validation?
• What do recent FDA authorizations and Commissioner comments signal about the agency’s direction?
• How can regulated companies align with FDA expectations while pursuing patient-focused development?

This webinar benefits the following agencies -

The session is built around the U.S. FDA’s evolving, risk-based policy statements on the use of AI and machine learning in drug discovery, development, production, and post-market monitoring. It is relevant to pharmaceutical and biotechnology organizations adopting AI in FDA-regulated work.

Who should attend?

This webinar benefits professionals tasked with pharmaceutical development responsibilities, including:

• Senior Management in Pharmaceuticals
• Quality Assurance (QA) / Regulatory Affairs (RA)
• AI software programming, documentation, and testing teams
• Research & Development (R&D)
• Engineering; Production; Operations
• Marketing
• Consultants and others tasked with pharmaceutical development