A New York City hospital is building a digital copy of your body to try to catch disease before you ever feel sick.
Quick Take
- Mount Sinai Hospital has launched the DigiTwin Study, tracking about 1,000 people for two years using artificial intelligence and real-time data.
- The project aims to flip medicine from treating illness after it strikes to spotting warning signs beforehand.
- Researchers combine imaging, lab tests, wearable device data, and lifestyle habits into one digital model for each patient.
- Independent research reviews show most AI health-prediction models never reach everyday use, a gap worth watching as this trial unfolds.
Mount Sinai Launches Digital Twin Trial
NBC’s TODAY show reported on October 22, 2026, that researchers at Mount Sinai Hospital are launching an ambitious project. The goal: shift patient care “from treating sickness to predicting and preventing it,” using what the segment called cutting-edge artificial intelligence and real-time data. The reporting marks one of the most public showcases yet of a hospital system betting on prediction over treatment.
Behind the TV segment sits a formal research effort. Mount Sinai’s BioMedical Engineering and Imaging Institute describes the DigiTwin Study as a plan to collect and combine imaging scans, laboratory tests, wearable device readings, and lifestyle information into one large digital health record for each participant. The study is still enrolling, and no results have been published yet.
How the Digital Twin Model Works
The underlying effort, called the DigiTwin Project, is led by Dr. Zahi Fayad and sits inside Mount Sinai’s broader Healthspan Program. That program’s stated aim is to slow aging at its biological source and prevent conditions like cancer, diabetes, heart disease, and Alzheimer’s before they take hold. Rather than waiting for symptoms, the system tries to flag risk years earlier through continuous data tracking.
Mount Sinai runs related efforts under the same umbrella. Its NYC-Vita trial, part of the national XPRIZE Healthspan competition, tests whether a drug, a nutritional supplement, and supervised exercise together can prevent or reverse age-related immune decline. Together, these programs reflect a hospital-wide push to treat aging itself as something doctors can measure and slow down, not just manage after disease appears.
Why Experts Urge Patience Despite the Hype
Health researchers who study digital twins broadly caution that the technology remains early-stage. A review of artificial intelligence models built to predict patient outcomes found that 86% had only been tested internally, 11% had outside validation, and just 3% had actually reached deployment in real clinics. That gap between promising models and working tools is the biggest hurdle facing any prediction-based health system.
A separate scoping review of human digital twins found recurring obstacles across the field: data privacy concerns, inconsistent validation standards, and uneven access to the technology. A broader umbrella review of digital twin research echoed those same themes, noting that real-time predictive modeling shows promise for chronic disease management but still faces real barriers before widespread use. Mount Sinai’s trial will need to clear those same hurdles to prove its model works outside the lab.
For now, the DigiTwin Study stands as a large-scale test of whether a hospital can turn mountains of personal health data into early warnings patients can actually act on. Whether it becomes a model other hospitals copy, or another example of healthcare technology that moves slower than the headlines suggest, will depend on results still years away.
Sources:
youtube.com, mountsinai.org, bmeiisinai.org, digitwinhealth.com, reports.mountsinai.org, frontiersin.org, link.springer.com, jmir.org
