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<p>Biopharma and medtech explained. Every week, journalists from Fierce Biotech, Fierce Medtech, and Fierce Pharma recount the latest industry trends and why they matter. We'll analyze the week's bigg...
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“Digital twins can be used to predict how a patient would respond to different treatments by leveraging their existing medical history and disease response data.”
— Nadea Leavitt, Clinical Statistician and AI Researcher · 11/16/2026
“Digital twins help researchers design better studies, evaluate trial strategies, generate individualized predictions, and in some scenarios, even create comparator cohorts, especially when traditional control groups are difficult to obtain.”
— Nadea Leavitt, Clinical Statistician and AI Researcher · 11/16/2026
“IQVIA Life Science models are a family of foundation models that are powered by multimodal data that create a richer digital twin and make patient-level simulation scalable across diseases and use cases in a way that was previously very difficult.”
— Nadea Leavitt, Clinical Statistician and AI Researcher · 11/16/2026
“Life Science models learn from longitudinal real-world data on more than 200 million patients to understand how diseases progress, how patients respond to treatment, how clinical events unfold over time.”
— Nadea Leavitt, Clinical Statistician and AI Researcher · 11/16/2026
“If a digital twin is going to influence a trial design decision, a regulatory submission, or ultimately patient access to therapy, then the question isn't whether the model is sophisticated, the question is whether it's fit for purpose we're asking it to serve.”
— Nadea Leavitt, Clinical Statistician and AI Researcher · 11/16/2026
“You have a real patient with their medical history and how their disease responded to a specific treatment, but what you want to know is how this same patient would respond to a different treatment. This is where digital twins come in.”
— Nadea Leavitt, Clinical Statistician and AI Researcher · 11/16/2026
“A digital twin is created or initialized from data about a real patient, and depending on the use case, can actually be updated or re-estimated as the real patient's clinical state evolves and new data becomes available.”
— Nadea Leavitt, Clinical Statistician and AI Researcher · 11/16/2026
“These combine biological knowledge with data-driven learning, often using things like knowledge graphs, ontologies, or mechanistic constraints to keep predictions clinically plausible or to help model scenarios that are not represented in real-world data, while still benefiting from the scale and flexibility of modern AI.”
— Nadea Leavitt, Clinical Statistician and AI Researcher · 11/16/2026
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