Advancing AI and integrated scientific learning approaches across the drug development lifecycle.
AI/Integrated Scientific Learning
Overview
The AI/Integrated Scientific Learning Sub-team is chaired by Alex Sverdlov, Ph.D. (Novartis), with members Maha Karnoub and Vanja Vlajnic. The sub-team focuses on how machine learning, AI, and integrated scientific learning approaches can be applied in drug development, including drug repurposing, biomarker identification, patient selection, and trial simulation.
Completed Work
Chapter 11: Machine Learning/Artificial Intelligence in Drug Development, Karnoub M, Just F, Yin J, and Vlajnic V (Daiichi Sankyo; Bayer Healthcare Pharmaceuticals). In: Statistics in Clinical Development of Cancer Drugs: Recent Trends & Advances (Eds. Kundu & Ghosh). Springer. In press.
Current Projects
A manuscript on AI/ML applications for drug repurposing in rare diseases is currently in preparation, in collaboration with the NEED Sub-team.
Relevant Guidance
The January 2026 FDA draft guidance on Bayesian Methodology in Clinical Trials is directly relevant to this sub-team’s work, particularly the sections on incorporating real-world evidence and external data sources. See the Resources & Guidance page.
Get Involved
To get involved, email the IDSWG at asadahshuidswg@gmail.com and your message will reach the sub-team lead.
Page last reviewed: