Mount Sinai DigiTwin

How Digital Twins Revolutionize Health

Advancements in AI and Machine Learning (ML) now allow us to analyze massive amounts of data and create virtual health models, or Digital Twins. These models act as real-time simulations of your body, monitoring key health indicators and predicting risks with incredible accuracy.

Our two key objectives for Digital Twin technology in healthcare:

1. Real-Time Health Tracking & AI-Powered Guidance – A digital health companion that provides expert-level insights into your well-being.
2. Early Intervention for Preventive Care – Proactively identifying risks before symptoms appear, reducing the need for expensive treatments later.

Starting with Cardiovascular Health—Then Expanding to All Areas of Wellness

Heart disease is the leading cause of death worldwide, yet current prevention strategies rely on outdated risk assessments. Many approaches overlook key factors like inflammation, genetics, and emerging biomarkers, leading to one-size-fits-all solutions that don’t work for everyone.

Our Digital Twin technology personalizes prevention by integrating advanced data analysis, real-time biomarker tracking, and AI-driven insights. This means more precise predictions, better interventions, and healthcare tailored to you. While we’re starting with cardiovascular health, this model can be expanded to optimize all aspects of wellness.

Data is the key

Mount Sinai is undertaking a pioneering new study to develop the tools and data modeling needed to transform healthcare using Digital Twin models. Our goal is to amass a comprehensive profile of health data in a cohort of 1,000 individuals within the next 3-5 years. The health data that will be collected will span established clinical laboratory and physical function tests, state-of-the-art whole body MRI imaging, state-of-the-art genetics and multiomics tests, comprehensive lifestyle questionnaire data collection, and use of novel at-home sensors and devices for monitoring activity and health. The depth and breadth of health measures is designed to capture molecular, cellular, organ-level, and organism level data so we can understand how systems interact with each other and impact overall health.

Putting it all together

Once we have assembled the Heart Health Digital Twin cohort data, we will develop next-generation AI and ML modeling to interpret the data. Sophisticated machine learning analysis will perform individual phenotyping to discover novel insights into health at any one moment and trajectories in health markers over time – critical for meeting the objective of real-time health tracking and recognizing the signs for preventive intervention. Next, we will use AI to learn how lifestyle and health interact and develop models that provide personalized feedback to optimize the effect lifestyle has on health – meeting our objective to provide adaptable, expert-level guidance.

What’s Involved

The data collected for the study will encompass the molecular, cellular, and organ-level function of all the key organs and organ systems in the body including the brain, heart, lungs, liver, pancreas, and gut. Other tests will provide data on the individual as a whole.