ANO Digital Economy has released a report on trends in the use of AI in medicine.

In compiling the report, ANO Digital Economy classified the trends by time categories: short-term trends (currently 2 years; currently being implemented, but not yet covering all companies and industries), medium-term trends (3-5 years; currently in the pilot project and partial implementation phase without large-scale dissemination), long-term trends (5 years or more; concepts and technologies that are only being discussed or implemented in limited projects, as their implementation is difficult).

Intelligent assistants, such as the Russian DIMA (MD AI) and the American Microsoft DAX Copilot, will be used today and in the next two years to support healthcare professionals in making diagnoses and selecting treatment methods. According to the report, this will address the problems of physicians being overburdened with "routine administrative work and documentation" and the lack of efficiency and accuracy in collecting medical history and initial diagnostics due to human error.

Furthermore, the ANO believes that devices for monitoring health indicators and conducting diagnostic tests will soon become more widespread. These include smart devices, fitness trackers, self-checking devices, and remote diagnostics. These devices will make it easier and more accessible to monitor patients' conditions, especially for people with chronic illnesses and those who find it difficult to regularly visit a healthcare facility.

The report cites examples of monitoring devices: Neyrox, from a Russian manufacturer, tracks parameters such as heart rate, ECG, respiration, temperature, glucose levels, and oxygen saturation, as well as nervous system responses. Using AI, Neyrox analyzes this data and predicts epileptic seizures 40-50 seconds before they begin, subsequently alerting the user. Empatica's EmbracePlus device for monitoring physiological parameters and early detection of diseases, including COVID-19, is also mentioned.

Electronic medical records (EMR/EHR) will solve the problem of the lack of a unified database, which hinders communication between medical institutions. They will also eliminate human error and save time for patients and doctors. ANO cites as examples the Russian "Electronic Medical Record" with AI for data analysis and decision support, and a development by the American company Epic Systems.

Over the next 3-5 years, generative AI (for generating new data) will be implemented; its use in medicine is "changing approaches to diagnosis, treatment, and drug development." Analysts believe that the use of this type of artificial intelligence will address the lack of support for physicians in making diagnoses and choosing optimal treatments, as well as the limitations of traditional methods for analyzing large volumes of data. Technologies using generative AI include the Russian Syntelly platform, which analyzes the toxicological and physicochemical properties of compounds, and Insilico Medicine's drug development technology.

The report's authors also highlighted the trend toward creating customized health insurance programs using artificial intelligence. Using personalized insurance products that analyze each client's unique medical data and behavior, insurance companies will be able to offer flexible rates and programs. These programs will be tailored to the needs and risks associated with human factors. This will improve the quality of service and the effectiveness of insurance. The organization cites as an example the SberHealth insurance platform and Lightbeam Health, which analyzes over 4,500 factors, including clinical, social, and environmental factors, to identify hidden risks.

Among the long-term trends highlighted are autonomous AI agents—programs that independently analyze patient medical data. They are also capable of making diagnoses, recommending treatments, and performing certain procedures without direct physician intervention. Autonomous AI agents operate using large volumes of data and machine learning algorithms, which will help automate routine processes and decision-making, speeding up responses to changes in patient conditions. Examples of this technology include Grace from Hippocratic AI.

Personalized medicine based on AI and genomics is also a long-term trend. It combines a person's genetic data with clinical indicators. Analysts claim that developing personalized treatment regimens based on individual characteristics will improve the effectiveness of therapy and reduce the risk of side effects. An example of this is the Canadian startup Deep Genomics, which uses AI to predict the impact of genetic mutations and develop targeted drugs.

In addition, AI-controlled implants and neuroprosthetics, which are capable of continuously collecting and analyzing physiological parameters, automatically adjusting their operation, and preventing the development of complications, have become long-term trends.

According to the report, AI technologies for epidemiological monitoring and early warning of infectious disease outbreaks have become a trend that could be realized in five years or more. As an example, the Canadian company BlueDot is cited, which was the first in the world to warn about COVID-19 in December 2019 by analyzing millions of data sources using AI.

Analysts believe that digital patient twins will only become widely used in the long term. To create them, virtual models are being developed with information about the patient's health and prescribed treatments. Among those working on this technology are Sechenov University and the startup Q Bio Gemini.

Long-term trends also include the use of virtual and augmented reality (VR/AR) technologies in various fields, "from medical personnel training and surgical preparation to rehabilitation and treatment of various diseases." Developers include Samara State Medical University with its modular expert virtual system "MEVIS" and FundamentalVR's surgical procedure simulation system.

Surgical robots have also become a long-term trend, potentially reducing the risk of errors and limited precision in traditional surgical procedures. In Russia, for example, the surgical robot "Levsha" was developed by the company "Neurosputnik," which allows the surgeon to "feel" the manipulations, just like in traditional surgery.