EXECUTIVE SUMMARY
Digital twins in healthcare are computational models that simulate an individual patient's physiology, enabling personalized treatment planning, drug response prediction, and virtual clinical trials. The technology ranges from organ-specific models (cardiac, metabolic) to whole-body simulations. HeartFlow's cardiac digital twin, deployed across 725+ hospitals, exemplifies clinical adoption, while Dassault Systèmes' Living Heart Project has enabled FDA device approvals without physical testing.
What Are Healthcare Digital Twins?
A digital twin in healthcare is a virtual representation of a patient's biological system—whether an individual organ, physiological process, or entire body—that can be used to simulate responses to treatments, predict disease progression, and optimize therapeutic interventions.
TYPES OF HEALTHCARE DIGITAL TWINS
- Organ-Specific: Heart, brain, liver models simulating organ function and drug response
- Physiological System: Cardiovascular, metabolic, immune system simulations
- Patient-Specific: Individualized models calibrated to patient data for personalized medicine
- Virtual Population: Synthetic patient cohorts for in-silico clinical trials
Cardiac Digital Twins: The Leading Application
HeartFlow
Non-invasive cardiac digital twin derived from CT scans. Simulates blood flow (FFR) to diagnose coronary artery disease. IPO August 2025 (HTFL). $125.8M 2024 revenue (44% YoY growth), 75% gross margin.
High-fidelity cardiac simulation launched 2014. 100+ partners including FDA. Enabled medical device approvals via simulation. FDA Enrichment Playbook (Oct 2024).
AI-powered cardiac models. Partnership with UCSF for Alzheimer's digital twins (March 2025). Integration with medical imaging systems.
Digital Twin Applications Beyond Cardiology
Whole-body metabolic digital twin for Type 2 diabetes management. Demonstrated disease remission in clinical studies through personalized nutrition and lifestyle optimization.
TwinRCT platform creates digital twins of control arm patients. EMA qualification for Phase 2/3 trials. Reduces trial size requirements by generating synthetic control data.
First digital twin generated from organ-on-chip data (July 2025). Bridges experimental MPS data with computational models for predictive pharmacology.
Physiologically-based pharmacokinetic models simulate drug distribution. Established regulatory acceptance for dose optimization. Companies: Certara, Simulations Plus.
FDA Collaboration & Regulatory Acceptance
FDA ENRICHMENT PLAYBOOK (OCTOBER 2024)
FDA and Dassault Systèmes published a 44-page guide for using computational modeling in medical device development. The playbook provides frameworks for:
- → Virtual clinical trial design and execution
- → Model verification and validation standards
- → Credibility assessment for regulatory submissions
- → Integration of simulation with physical testing
Digital twins have already enabled FDA medical device approvals without physical clinical trials. The Living Heart Project has been used to simulate device performance in virtual patient populations, demonstrating safety and efficacy computationally. This regulatory precedent is now extending to drug development applications.
In-Silico Clinical Trials
Virtual clinical trials use digital twin populations to simulate drug effects across diverse patient groups. This approach can:
Reduce Trial Size
Synthetic control arms reduce patient recruitment needs by 30-50%
Accelerate Timelines
Virtual trials complete in days vs. years for physical trials
Improve Diversity
Test across synthetic populations representing underrepresented groups
Ethical Advantage
Reduce exposure of real patients to potentially harmful compounds