Basic
Emerging
Claire Neilan, PhD, DABT
SVP Preclinical Sciences
IDEAYA Biosciences, Inc.
San Francisco, California, United States
Zheng Li, PhD, DABT
Pharmacologist
US FDA
Silver Spring, Maryland, United States
Peyton Myers, PhD, DABT
Supervisory Interdisciplinary Scientist
CDER, OND
US FDA
Silver Spring, Maryland, United States
Yodi Melnikov, PhD
Senior Principal AI Scientist
Genentech
South San Francisco, California, United States
Lise Bertrand, DVM MSc DiplECVP
Scientific Director, Digital Pathology
Charles River Laboratories, France
.jpg "Kamel Mansouri, PhD photo")
Kamel Mansouri, PhD
Staff Scientist
NTP Interagency Center for the Evaluation of Alternative Toxicological Methods
National Institutes of Health
Kevin Snyder, PhD
Certara
Gaithersburg, Maryland, United States
This CE course will offer a comprehensive examination of the application of Artificial Intelligence (AI) in toxicology, emphasizing its transformative potential and challenges within regulatory decision-making. Participants will gain foundational knowledge of AI concepts, explore state-of-the-art methodologies for toxicology predictions, and examine case studies showcasing successful AI applications. Additionally, the course will address current barriers to AI adoption and discuss strategies to integrate AI innovations into regulatory practices. By the end of this course, participants will: Understand foundational AI concepts and methodologies as applied to toxicology; Evaluate the strengths and limitations of AI tools in regulatory contexts; Critically assess AI-based toxicology predictions and their implications; Explore ongoing developments, ethical considerations, and collaborative opportunities in AI-driven toxicology. The course aspires to empower attendees to leverage AI for improved toxicological assessments while addressing regulatory challenges, ultimately driving safer and more efficient decision-making processes.