The use of artificial intelligence in healthcare is no longer unusual. Today, the global AI in medicine market is valued at $22 billion, while in Russia its volume reaches 12 billion rubles. Analysts forecast that these figures will multiply by 2030. This article explores how new technologies are transforming the work of scientists, doctors, and the patient experience.
Growth zone
By 2030, the global AI in healthcare market is projected to grow 6.5-fold – from $22 billion to $130-160 billion. A similar surge is expected in Russia: from 12 billion rubles in 2024 to 78 billion by 2030. This growth is driven not only by technological progress but also by increasing demand for innovations from both doctors and patients, according to a joint study by Yakov & Partners and the Medsi group of clinics, the results of which are available to the editorial board.
“Artificial intelligence in healthcare has long been part of real clinical practice. While it was previously used mainly for medical image analysis and predictive analytics, since late 2022, thanks to new-generation generative models, AI’s capabilities have expanded significantly,” notes Nikita Vlasov, a consultant at Yakov & Partners. “Today, AI not only analyzes images but also works with medical history, forms personalized recommendations, and interacts with patients as a digital assistant.”
Under these conditions, major players are already changing approaches to clinic-client interaction, the expert emphasizes. For example, Hippocratic AI has introduced a marketplace of digital physicians consulting on post-operative care and medication. And Microsoft, in partnership with Nuance, launched Dragon Copilot – a system that saves a doctor up to 7 minutes per appointment by automatically recording key elements of the patient dialogue in real time.
“Solutions ready for large-scale application are also emerging on the Russian market. Our research analyzed over 80 AI products. Four areas showed the greatest potential: digital assistants, clinical summary modules, physician assistants, and expert control systems. In the next 5 years, these product groups could form a market in Russia with a potential of 65 billion rubles,” forecasts Nikita Vlasov.
The prototype
Even today, AI is deeply integrated with the industry. One of the most striking examples of AI application in healthcare is the experience of Russian ophthalmologists. As Youssef Youssef, Director of the Krasnov Research Institute of Eye Diseases, stated at the V International Forum “Ophthalmo-gerontology – Innovative Solutions to Problems,” such technologies are now actively used for the diagnosis, monitoring, and treatment of retinal diseases. He noted that AI technologies will only gain popularity given that retinal problems are more common in older adults and the population is rapidly aging.
As Sergey Zhdanov, Director of the Health Industry Center at Sberbank, emphasizes, AI is fundamentally changing the very paradigm of medicine. He states that by 2029, AI agents will become as commonplace as smartphones. The transition to a personalized treatment approach is also impossible without AI, believes Yevgeny Shlyakhto, General Director of the Almazov National Medical Research Centre. He stated this at the St. Petersburg Medical Innovation Forum. This new treatment model is based not on universal therapy but on precise, individualized treatment strategies based on the analysis of large volumes of data, including genetic information.
“Undoubtedly, implementing this approach requires significant investment in infrastructure, but the result of this work will be increased diagnostic accuracy, treatment efficiency, and reduced costs,” the expert added.
A recent work by Dr. Dmitry Chebanov (Memorial Sloan Kettering Cancer Center) and his team, led by Professor Quaid Morris, further confirms that the future lies in personalized medicine and neurotechnologies. They presented an AI-based system capable of creating digital twins for patients with rare cancers. These virtual profiles compensate for the lack of real data and allow for the development of treatments that were previously impossible. Algorithms trained on synthetic patients showed outstanding results: the diagnostic accuracy for some rare tumors exceeded that of similar systems trained on real data by more than 10 times.
AI risks
In Russia today, over 40% of IT solutions in medicine incorporate AI, as previously reported by Skolkovo. Meanwhile, the number of startups related to neurotechnologies continues to grow, as does their revenue. According to Kamila Zarubina, Director of the Biomedical Technology Center at the Skolkovo Foundation, the annual monetary growth in this area exceeds 35%.
This trend is also pronounced globally: in just the first 2 months of 2025, investment in medical AI startups exceeded $2 billion, nearly reaching the level of the entire Q1 2024 ($2.8 billion). If the current trend continues, total investment in digital healthcare could reach $25-30 billion by the end of the year, predict Yakov & Partners.
However, healthcare remains one of the most challenging sectors for AI implementation. “Mistakes here can lead not only to reputational damage but also to real risks to patient lives,” notes Nikita Vlasov.
“Generative models are susceptible to vulnerabilities: they can distort data, uncritically accept input information, and fail to recognize context. These risks can be reduced through testing, prompt tuning, and fine-tuning, but such processes require time and significant resources,” the expert added.
The next 3-5 years will be decisive for the industry, he believes. Those who are first to bring clinically validated solutions to market will gain a sustainable advantage and set a new benchmark for the quality of medical care.
“Therefore, it is important for companies working with medical AI solutions to build a strategy considering several key factors. First, it is necessary to plan investments and manage investor expectations, focusing on realistic timelines for product launch, obtaining medical device status, and gradual growth of the customer base. Moreover, it is critical to control the entire product creation chain, paying special attention to the quality of data used to train models,” advised Nikita Vlasov.
An integral part of the strategy, he said, should be the creation of a testing platform for solutions. This will allow for obtaining multifaceted feedback, including the opinions of doctors from various specialties.
