With decades of experience across clinical planning, patient identification, and recruitment strategy, Citeline brings together data, Al, and human expertise to help sponsors find the right patients — sooner. This session covers the practical strategies and end-to-end capabilities that are making recruitment faster, more precise, and more predictable.
Madeline Naylor
Chief Clinician, Clinical & Regulatory Strategic Intelligence
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Chief Clinician, Clinical & Regulatory Strategic Intelligence
Citeline
Madeline is a Doctor of Health Science with a Master of Medical Science, and holds a Clinical Research Coordinator certification from the Association of Clinical Research Professionals. Before joining Norstella she spent 10 years managing and conducting academic and commercial research in hospitals and clinics, helping lead the largest early-onset Alzheimer's studies globally and launching a cardiovascular center's first surgical device trial. She specializes in neurological research with broad experience across disease areas and a particular interest in oncology, and now combines clinical and real-world data expertise to shape the solutions customers and their patients rely on.
Suzanne Caruso
President, Citeline Strategic Intelligence, Clinical & Regulatory
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President, Citeline Strategic Intelligence, Clinical & Regulatory
Norstella
I’ve spent more than 15 years helping pharmaceutical companies plan and operationalize clinical trials, with leadership roles across Novartis, GSK, WCG, Veeva, and now Norstella. Having worked across clinical operations, data and strategic intelligence, I’ve seen firsthand where AI can remove friction from development and where human expertise remains essential.
What makes Norstella's AI different?
It starts with decades of proprietary clinical, pipeline, and real-world data already organized around the way drug development works. This provides industry-specific context rather than generic information. Transparent sourcing and expert oversight make it possible for users to understand the evidence behind an AI-generated insight.
What can pharma teams do now that was not possible two years ago?
AI agents can handle many of the manual steps between posing a question and arriving at a usable answer. Fragmented information can be turned into a sourced, evidence-based output in minutes rather than days. AI combined with real-world data also allows teams to test study assumptions, identify patient populations, and refine recruitment strategies at much greater speed and scale.
How can AI accelerate the journey from pipeline to patient?
Drug development involves numerous handoffs across systems, teams, and decisions. AI can connect those workflows, automate routine analysis, and surface relevant information at the right moment, leaving specialists to concentrate on the decisions requiring their expertise.
