How Digital Twins Could End Medical Guesswork With: Jim St. Clair and James Fargason
Digital twins are emerging as one of the most powerful tools to reduce uncertainty in medicine, particularly in oncology, where clinicians are often forced to make high-stakes decisions with incomplete data. In this full interview, Jim St. Clair and James Fargason explore how digital twin technology could fundamentally reshape healthcare by creating dynamic, data-driven virtual representations of patients that evolve in real time.
Drawing on advances from engineering, infrastructure, and aerospace, the conversation examines how patient-centric digital twins could integrate genomics, pharmacogenomics, environmental exposure, clinical history, and real-world data to improve treatment selection, reduce toxicity, and accelerate clinical trials. The discussion highlights why digital twins go beyond static AI predictions, emphasizing continuous feedback, simulation, and probability modeling.
The guests also address critical challenges, including data fragmentation, ownership, trust, and clinician adoption. As healthcare moves toward precision medicine and value-based care, digital twins may become indispensable tools for clinicians, researchers, and patients seeking more personalized, evidence-driven decisions.
Episode Contents:
About the Guest
Jim St. Clair is Chief Trust Officer for the Lumedic Exchange and a leader in healthcare data standards, interoperability, and digital identity. He actively contributes to HL7, ISO, and IEEE initiatives and advises global efforts advancing patient-centric data exchange.
James Fargason is a faculty member, serial entrepreneur, and co-founder of Digi Twin Global, with decades of experience advising Fortune 500 companies and startups on value creation, analytics, and digital innovation.
Key Takeaways
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Digital twins integrate real-time data to personalize treatment decisions.
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Healthcare digital twins mature through iterative, high-impact use cases.
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Data quality and trust determine whether digital twins improve outcomes.
Transcript Summary
How do you define a digital twin for medicine?
A digital twin is a dynamic virtual representation of a physical system that updates continuously with real-world data. In healthcare, this means modeling a patient as a system of systems that can simulate responses to drugs, environment, and clinical interventions.
Why are digital twins important for clinicians?
Medicine often relies on population averages rather than individual biology. Digital twins allow clinicians to simulate outcomes for a specific patient, helping predict efficacy, toxicity, and risk before treatment begins.
How could digital twins change cancer care?
Digital twins can integrate tumor biology, pharmacogenomics, immune response, and patient physiology to personalize chemotherapy, radiotherapy, and trial enrollment while reducing unnecessary harm.
What role do digital twins play in clinical trials?
Digital twins can reduce control arm sizes, identify underrepresented populations, accelerate timelines, and simulate outcomes that would otherwise take years to observe.
What limits digital twin adoption today?
Healthcare data remains fragmented across systems, formats, and ownership models. Without high-quality, interoperable data and clinician trust, digital twins cannot reach their full potential.
How does AI fit into digital twin ecosystems?
AI powers pattern recognition and prediction within the twin, but the digital twin itself orchestrates multiple models, disciplines, and data sources into a coherent system.
Who should own a patient’s digital twin?
Ownership, consent, and access remain unresolved questions. Ensuring patient-centered governance is critical to preventing misuse by insurers or third parties.
More Topics
- AI in Patient Care
- AI in the Healthcare Industry
- AI and Medical Innovation
- Healthcare Ethics and Policy
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About the Series
AI and Healthcare—with Mika Newton and Dr. Sanjay Juneja is an engaging interview series featuring world-renowned leaders shaping the intersection of artificial intelligence and medicine.
Dr. Sanjay Juneja, a hematologist and medical oncologist widely recognized as “TheOncDoc,” is a trailblazer in healthcare innovation and a rising authority on the transformative role of AI in medicine.
Mika Newton is an expert in healthcare data management, with a focus on data completeness and universality. Mika is on the editorial board of AI in Precision Oncology and is no stranger to bringing transformative technologies to market and fostering innovation.