InfraTwin AI creates living digital twins of patients, from individual organs to full physiological systems. We combine clinical data, imaging, and wearable signals into a virtual model, helping clinical teams see how diseases progress and treatments respond before making real-world medical decisions.
Medicine today treats populations, not individuals. Drug dosages follow averages. Treatment protocols assume standard responses. Disease progression is monitored through periodic snapshots — lab results taken days apart, imaging done weeks apart. Between those snapshots, the patient's body is a black box. Clinicians are forced to react to symptoms that have already escalated, rather than intervening at the earliest signal of change.
When clinical decisions are guided by a continuously updating virtual replica of each patient, outcomes improve measurably — not through guesswork, but through mathematically modelled physiological behaviour. These are the results most healthcare organizations see.
The patient twin integrates only the signals that influence diagnostic accuracy, treatment response, disease progression, and care outcomes. These data streams form the foundation for predictive, personalized clinical intelligence.
InfraTwin AI ingests data from every layer of the patient's clinical reality — wearable devices, medical imaging systems, and electronic health records. These diverse signal streams are fused into a single, continuously updating patient digital twin that gives clinicians a complete physiological picture in real time.
Smartwatches (Apple Watch, Fitbit), continuous glucose monitors (CGMs), Holter monitors, smart patches, and pulse oximeters stream real-time heart rate, HRV, SpO₂, activity, sleep quality, blood glucose, and skin temperature directly into the patient twin — capturing the patient's physiology between clinic visits.
MRI scans, CT scans, ultrasound, echocardiograms (Echo), X-rays, and PET scans are reconstructed into patient-specific 3D anatomical models. Surgeons rehearse procedures on exact organ replicas. Oncologists visualise tumour positioning. Cardiologists simulate interventional pathways — all derived from the patient's own imaging data.
Electronic Health Records (EHR/EMR), lab results, pathology reports, prescription history, and genomic sequencing data are structured and time-aligned within the twin. Pharmacogenomic profiles predict drug response. Biomarker trends reveal disease progression. Every clinical signal is unified into one longitudinal patient model.
The patient twin follows a clear, engineered process. We build the ideal physiological model, capture the right clinical signals, reconstruct patient anatomy in 3D, run predictive AI on top, and give your clinical team an XR workspace for collaborative decisions.
Physiological Performance Blueprint
We mathematically define how each patient's body should function at its optimal state. Using clinical baselines, genomic data, age-adjusted norms, and population health models, we build a profile representing the patient's ideal physiological performance. This becomes the benchmark the digital twin uses to detect deviation, predict deterioration, and guide treatment.
AI-Driven Clinical Data Discovery
Every patient generates thousands of data points. Most of them are noise. InfraTwin AI identifies the specific clinical variables that actually explain disease behaviour, treatment response, and risk trajectory. We use machine learning to surface which biomarkers, vitals, and genomic markers matter most for each condition — so the twin monitors what counts.
Visualizing IoT, Wearables & Clinical Data Integration
IoT, Wearables & Clinical Data Integration
The digital twin depends on continuous, accurate observation. We integrate data from wearable biosensors, continuous glucose monitors, cardiac monitors, pulse oximeters, and clinical imaging systems. EHR data, lab results, and physician notes are ingested and structured. Every signal is time-stamped and spatially aligned to the patient's virtual anatomy.
Visualizing Photorealistic 3D Patient Reconstruction
Photorealistic 3D Patient Reconstruction
Using CT, MRI, and ultrasound imaging data, InfraTwin AI builds photorealistic 3D reconstructions of patient-specific organs and anatomical structures. These aren't generic models — they are exact replicas of the individual patient's heart, brain, lungs, or skeletal system. Surgeons can rehearse procedures. Oncologists can visualize tumour positioning. Cardiologists can simulate interventional pathways.
Visualizing Predictive Clinical AI Models
Predictive Clinical AI Models
The digital twin continuously compares real patient behaviour against the mathematically defined ideal state. AI models detect when physiological parameters begin drifting toward risk thresholds — days or weeks before clinical symptoms appear. The system simulates treatment options on the virtual patient, predicts drug response, flags contraindications, and recommends the most effective intervention path.
Visualizing XR-Based Clinical Workspace
XR-Based Clinical Workspace
The patient digital twin becomes operational when clinical teams can step inside it. InfraTwin AI provides an extended reality (XR) environment where surgeons, physicians, and specialists can collaboratively explore patient anatomy, review AI-generated risk forecasts, rehearse procedures, and align on treatment strategy — all within a shared, immersive 3D workspace.
Observation agents ingest real-time data from wearable devices (Fitbit, Apple Watch, CGMs, Holter monitors), process medical imaging (MRI, CT, Echo, ultrasound) into structured spatial data, and parse EHR/EMR records, lab results, prescription history, and clinical notes. Every signal is time-stamped and spatially aligned to the patient's 3D anatomical twin.
Reasoning agents detect physiological drift before symptoms appear — subtle HRV changes signalling cardiac risk, gradual biomarker shifts indicating disease progression, or metabolic patterns predicting adverse drug reactions. They correlate genomic profiles with drug metabolism, model disease trajectories across organ systems, and identify rare condition patterns through population-scale twin comparison.
Decision and governance agents translate patient intelligence into coordinated clinical actions. They recommend personalised treatment plans calibrated to the individual twin, simulate drug interactions on the virtual patient before prescription, generate pre-surgical rehearsal pathways on 3D anatomical models, and flag contraindications and compliance risks in real time.
See how the patient digital twin drives measurable improvements in clinical outcomes.
A global initiative that created a highly accurate, 3D digital twin of the human heart. Researchers and clinicians use this twin to simulate surgical procedures, test the placement of medical devices (like pacemakers and stents), and predict individual patient outcomes without physical trials.
Siemens Healthineers developed an AI-powered digital twin that integrates a patient's ECG, MRI, and clinical data to predict their response to Cardiac Resynchronization Therapy (CRT) for heart failure.
Mater Hospital in Dublin implemented a digital twin of its hospital operations (including radiology and bed management) to simulate patient flow and optimize resource allocation during peak demand periods.
Get in touch to learn how InfraTwin AI can help you deliver personalized, predictive medicine powered by patient digital twins.
Email Us
hello@infratwin.ai