Patient digital twins give life sciences teams a framework for evaluating how an individual patient’s health trajectory could change under different treatments or scenarios. In this sponsored episode of The Top Line, host Kelly Hogan speaks with Nadea Leavitt, a clinical statistician and AI researcher at IQVIA, about the role of digital twins in clinical development and evidence generation.

Leavitt explains the patient-specific connection that defines a digital twin, how the approach differs from external control arms, and how modeled predictions can complement observed comparator data. She reviews the spectrum of supporting analytics, from mechanistic and data-driven models to hybrid approaches that combine AI with biological knowledge and clinical constraints. The discussion also examines how IQVIA Life Science Models apply longitudinal real-world data, biomedical and clinical trial knowledge graphs, and multimodal patient information to simulate potential trajectories. Listen to the full podcast to learn more about the validation, transparency, scientific rigor and clinical plausibility required to build trust in model-generated evidence.