What Is a Digital Twin? Jason Alan Snyder on AI, Data, and Healthcare
Digital twins—AI-powered models based on individual health data—are poised to transform how healthcare predicts, treats, and personalizes care. But Jason Alan Snyder, a technologist and futurist, raises powerful ethical and technical concerns about how these systems are being deployed today. Built from EHRs, lab results, wearables, and even genomics, these digital representations can simulate decisions and even replace human judgment. Yet they are often created without consent or clarity. In this clip, Snyder urges healthcare leaders to reclaim agency over the design and governance of these digital surrogates, emphasizing that the future of AI in healthcare must be rooted in truth, transparency, and trust.
Episode Contents:
About the Guest
Jason Alan Snyder is a technologist, futurist, and inventor known for his visionary insights into human-machine systems, AI ethics, and innovation strategy.
Key Takeaways
- Digital twins are data-driven replicas that can simulate medical decisions.
- Current systems lack transparency, patient control, and ethical safeguards.
- Fragmented data can lead to dangerous inaccuracies in AI decision-making.
- Unified platforms like Palantir hint at both promise and peril.
- Trust and truth must guide future digital twin development in healthcare.
Condensed Transcript
Q: What exactly is a digital twin, and how should we think about them in healthcare?
A digital twin is no longer just a passive tool—it’s an agent built from our lab results, biometrics, genomes, and behaviors. It can simulate decisions, replace judgment, and act independently. That’s both powerful and potentially dangerous if patients aren’t part of shaping how it's used.
Q: Who’s building these systems and what are the risks?
Right now, systems like this are being developed by major platforms—without consent, clarity, or compensation. While innovation is racing ahead, regulation is lagging. We risk losing control unless we actively shape this future.
Q: Are there specific dangers in how health data is managed today?
Yes—data is fragmented across EHRs, labs, apps, and insurers. While this disunity can provide some safety from catastrophic breaches, it also hampers accuracy and coherence. Models hallucinate when data is incomplete, which is terrifying when making real-time care decisions.
Q: What’s your outlook on AI in healthcare?
Despite the risks, I’m optimistic. If we build systems based on truth, transparency, and trust, digital twins can extend our reach, amplify care, and protect autonomy. But we must evolve beyond current machine learning limits to achieve this vision.
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.