Textbooks for Physicians: Turning Russia's AI Experience into Training Guides for Radiologists
Russia has developed educational manuals on the use of artificial intelligence in medical imaging. The materials help clinicians learn how to work with AI services when analyzing CT scans, X-rays, and mammograms.
Moscow's Center for Diagnostics and Telemedicine of the Department of Health Care has developed and published two educational manuals focused on the use of artificial intelligence in radiology. The first is titled Application of Medical Devices Based on Artificial Intelligence Technologies in Radiology: Brain Computed Tomography, Chest Computed Tomography and Radiography, and Mammography. The second is Radiologist Workstation Simulator with Artificial Intelligence-Based Services.
Both publications are built on real-world clinical practice and are intended as practical tools for physicians. They allow users to observe how neural networks operate in a safe educational environment and help specialists develop the necessary competencies, from a basic understanding of computer vision to confident use of AI in clinical workflows. As a result, they can directly influence diagnostic quality and the speed of clinical decision-making.
Built on Real Clinical Experience
The manuals are designed to support medical students, residents, radiologists, medical informatics professionals, and healthcare administrators. They provide training in the use of AI services for the analysis, interpretation, and reporting of medical imaging studies.
The materials incorporate findings from Moscow's large-scale experiment in deploying computer vision technologies in healthcare, as well as experience gained through clinical validation and real-world use of AI-enabled medical products. They explain the fundamental principles behind neural networks in radiology, describe deployment models, and provide detailed recommendations for image analysis. Particular attention is given to both correct and incorrect algorithm outputs, assessment of diagnostic accuracy, and integration with the MosMedII (Moscow Medical AI Platform). The manuals also include exercises and review questions for self-assessment.
Accuracy and Time Savings
Today, Moscow's healthcare system uses more than 60 computer vision services across 45 clinical areas. These tools have processed more than 30 million imaging studies. Neural networks help identify signs of lung cancer, stroke, coronary artery disease, osteoporosis, pneumonia, aortic aneurysms, and other conditions on mammograms, CT scans, MRI studies, and X-rays. In some clinical applications, algorithm accuracy exceeds 95%.
AI has reduced the average time required to interpret imaging studies by approximately 30%. That has significantly lowered the workload for radiologists while accelerating diagnostic workflows. At the same time, final clinical decisions always remain with the physician.
For that reason, specialists believe these educational manuals will enable new generations of radiologists to enter clinical practice already prepared to work with AI, eliminating the need for lengthy on-site adaptation.
Access for Every Region
MosMedII, created following an initiative supported by President Vladimir Putin, has opened access to Moscow-developed AI solutions for healthcare organizations across the country. Today, the platform is used by more than 2,000 medical institutions in 75 Russian regions. In practice, that means a physician in a small city can obtain an additional expert assessment when reviewing medical images, improve diagnostic turnaround times, and deploy advanced technologies even when access to highly specialized experts is limited.
The educational manuals represent the next step in that process. They systematize Moscow's experience and make it available nationwide. Any region can use these materials to train healthcare professionals. That could significantly accelerate the spread of advanced clinical practices.
Export Potential
Moscow's experience in AI-assisted diagnostics is unique in scale. Few healthcare systems worldwide have evaluated so many algorithms in real clinical settings or integrated them into everyday practice across a metropolitan area serving millions of residents. Thus, the manuals effectively package that experience into a transferable educational product.
The materials could be translated and adapted for medical schools in other countries, particularly those at an early stage of AI adoption. Demand is likely because many healthcare systems face the same challenges, including shortages of radiologists, growing imaging volumes, and the need to improve diagnostic accuracy.
AI development in Russian healthcare is not limited to medical imaging. Neural networks are also assisting physicians during patient consultations. To date, AI systems have helped generate more than 15.6 million preliminary diagnoses with reported accuracy reaching 95%.
