Avatar Is No Longer Science Fiction: Russian IT Aims to Help Doctors See Risks Before They Escalate

A Strategy Built on Prevention

The initiative to create a Digital Twin of the Patient was proposed by the Association of Mammologists and Radiologists of the Republic of Dagestan, led by Honored Doctor of Russia Fatima Tamaeva. She presented a regional healthcare development strategy with a clear target – reducing disease incidence by 2030 through the deployment of digital patient models.

“Our objective is to reduce disease incidence by creating a Digital Twin of the Patient by 2030,” Tamaeva emphasized.

The idea did not emerge in isolation. In 2025, Fatima Tamaeva completed training at Singapore Management University under a program run by the Skolkovo Center for Healthcare Development. Exposure to international practice provided insight into how digital tools are already reshaping healthcare management in multiple countries, and helped adapt best practices to Russian clinical and regulatory realities.

At the same time, regulatory groundwork is being developed domestically. Researchers at Sechenov University and Peter the Great St. Petersburg Polytechnic University are drafting a national standard for digital twins in healthcare. This signals that the technology is moving beyond concept toward formal requirements covering accuracy, clinical validation and data protection.

What a Digital Twin Actually Means

Although the term may still sound futuristic to the general public, the concept is grounded in applied medical analytics. A digital twin is a virtual replica of a person’s organism or of a specific physiological system. It is built from real-world clinical data and allows clinicians to model how a disease may evolve, how a patient may respond to treatment, and what future risks could emerge.

This is not a cinematic “avatar,” but a practical analytics tool. The virtual model enables physicians to simulate scenarios. For example, it can project how the heart might respond to a specific physical load, how blood parameters could shift under a given therapy, or how multiple diagnoses might interact and affect overall health status.

Andrey Pozdnyakov, Chief Physician of the clinical diagnostic laboratory Invitro-Sibir, has previously noted that such systems enable evaluation of organ function across different disease states, account for comorbidities, and analyze potential progression of dangerous conditions. In effect, clinicians can “rehearse” treatment strategies in advance, without exposing patients to risk.

Why It Matters for Russia

The Dagestan project illustrates how IT and medicine are beginning to operate as an integrated system. Large-scale datasets, machine learning algorithms, clinical expertise and healthcare governance are converging within a single framework.

A digital twin is, in essence, a digital model of a real physical counterpart. It is a cyber-physical interaction model in which data collection about the physical object is so comprehensive and systematized that it enables prediction of all possible phenomena and characteristics over time through work with its digital twin

Nikita Utkin

Director of ANO Platforma NTI and Chairman of Technical Committee 194 “Cyber-Physical Systems”

For Russia, this represents a shift from a reactive healthcare model, where intervention begins after diagnosis, toward a preventive model. If a digital twin signals elevated disease risk, clinicians can recommend lifestyle adjustments, initiate monitoring, or start therapy earlier. This reduces pressure on hospitals, optimizes resource allocation and improves patient outcomes.

Preventing Disease Rather Than Reacting to It

Healthcare professionals consistently stress that prevention is more effective than treatment. For patients, a digital twin offers the opportunity to understand risk before symptoms appear. The model supports more precise diagnostics, a personalized care approach and a clearer picture of overall health. Over time, such systems could incorporate genetic data, chronic conditions, lifestyle patterns and even regional environmental factors.

This is particularly relevant in oncology and cardiovascular disease, where timing directly affects survival and quality of life. If the system alerts physicians to potential deterioration in advance, it can materially influence long-term outcomes.

Russia in the Global Context

Globally, investment in similar technologies is accelerating. In Europe, the European Virtual Human Twins Initiative is advancing digital patient modeling for personalized medicine. In Asia and the United States, major research programs focus on medical analytics and predictive modeling.

Russia’s approach emphasizes integration of domestic clinical expertise and IT development. The emergence of national standards, active involvement of leading universities and engagement of professional associations create the institutional basis for scaling these solutions.

With sustained policy and regulatory support, such systems could become part of routine clinical practice by 2030. That would strengthen Russia’s position in the increasingly competitive global market for medical IT solutions.