Флагманы и ревью

Флагманы

Machine learning technologies have a wide range of potential and real-world applications in healthcare. AI-based technologies directly help doctors do their work faster and of higher quality. For example, this category includes AI systems that work with medical images (X-rays, CT scans, MRIs, histological studies) - they compare studies over time, suggest the optimal dose of radiation, and generate a conclusion template for the radiologist. Such innovations help in the fight against COVID-19, which is one of the most important tasks in Russian health care.

AI can also potentially be used to control the quality of work of medical personnel and equipment (for example, whether a lab technician has positioned a patient correctly during an X-ray), treatment planning (selection of drugs and doses, selection of the right tests), automation of routine operations (automatic completion of patient records based on the results of an appointment). There are also more global solutions that are aimed at discovering new effective drugs or optimizing the management of entire hospitals.

Russia's implementation of AI in medicine compares favorably to many other countries. A striking example is the large-scale experiment on the use of computer vision technology in Moscow. Dozens of AI services are taking part in it. Domestic companies are not only actively implementing AI technologies in various Russian regions, but are also entering other markets.

Photo by the Press Service of the Mayor and Government of Moscow

AI helps fight cancer in Russia

The use of AI in the processing of radiological images allows the detection of signs of cancer. This approach helps reduce the risk of missing a pathology, especially with a large flow of studies and, accordingly, the burden on the radiologist.

In addition, AI systems can retrospectively review large databases of studies and find signs of cancer in the analyses. For example, during the pandemic, a huge number of chest CT scans were performed. The AI systems can quickly process them and perform triage - sorting by cancer risk.

What results has the Skolkovo resident achieved

The company Medical Screening Systems (Cels) has developed a number of solutions for analyzing medical images. The most successful of them is the system to analyze mammographic images (to search for signs of breast cancer and other pathologies on the images) and the system to detect signs of COVID-19 on chest CT.

It's still difficult to assess the economic impact of these systems, but feedback from physicians suggests that the systems are already making a real difference. The company notes cases where the system has found later confirmed signs of cancer on studies where the radiologist saw nothing. It is in the connection of the doctor and the ML-system that lies the main strength (the so-called human-in-the-loop scheme) of the new AI-based technologies.

At the end of 2021, the company signed an agreement with the Ministry of Health of Uzbekistan to introduce an AI-assisted radiology medical image processing system to the country's clinics.

«Cels» is actively involved not only in the development of technology but also in the development of new standards and regulations.

AI helps in the fight against coronavirus

Deputy Prime Minister Dmitry Chernyshenko at the international conference AI Journey 2021 reported that about 50% of all new AI-based MedTech services in Russia prioritize the diagnosis and treatment of COVID-19.

Back in the first wave of the pandemic, some developers of computer vision services participating in the Moscow experiment began collecting data to train AI to detect signs of coronavirus pneumonia on CT scans of the lungs. Already in April 2020, such systems began to be used in the capital's radiology departments (as part of the experiment).

A year later, the organizers of the experiment shared the results of using AI services to detect COVID-19. Over a year, the AI analyzed more than 730,000 CT scans and reduced the time for the doctor to read the scans by an average of 30%. Another useful feature is the automatic calculation of lung lesion percentages, which eliminates a significant portion of the routine.

Photo: Alexander Avilov / AGN Moskva

AI in medicine helps to cut costs

ML systems allow reducing the time a radiologist needs to interpret the examinations, thus reducing the cost per patient. This is especially important when there is a shortage of qualified personnel (e.g., in regions or developing countries). Early diagnosis of diseases, of course, saves a lot of money on treatment. For example, tens of billions of rubles are spent annually in Russia on the rehabilitation of stroke survivors. The use of AI in CT diagnosis of strokes allows the doctor to see the hemorrhage several times faster and begin the necessary clinical measures, which in some cases can lead to lower rehabilitation costs.

According to Evgeny Nikitin, head of the ML-development department at Cels, companies can sell their product only by proving that its use is justified. Several important factors in the economic feasibility of applying AI in medicine include:

The costs of developing and operating AI systems are still high. This has also been affected by the pandemic, which has led to higher salaries for ML and AI specialists because of the indiscriminate move of all companies to telecommuting-it is now possible to work from anywhere in the world for any company in the world;
The semiconductor crisis (graphics processors went up in price);
Medical tasks are much more complex than in other areas of AI applications. This factor affects the cost of their implementation - more experienced specialists, experiments, data, and computer resources are needed.

In the combination of these factors, we can say that the companies that have been able to set up their processes and tools so that the cost per experiment (e.g., testing a new neural network architecture) is minimal will survive.

How will patients benefit from the use of AI?

Russian citizens in the near future will be able to feel the benefits of the introduction of AI in medicine themselves:

There will be less time for patients to receive diagnosis information by automating routines and reducing the physician's workload;
The quality of diagnosis and treatment will improve;
The patient will undergo fewer tests and procedures;
In the long term, healthcare will become cheaper.

How the field of MedTech is developing in Russia relative to the rest of the world with the use of AI

In terms of the pace of MedTech development, Russia is at the forefront. The Moscow experiment, the extensive work to write standards and regulations as a whole, the growth of the telemedicine market, the support of the government - all these are quite visible results of development in the industry. The quality of Russian systems (according to published quality metrics) allows them to compete in the international market.

According to Evgeny Nikitin, Russia has an advantage compared to Western countries, Japan and South Korea in terms of data marking by doctors. In Russia, such work is relatively inexpensive, which makes it possible to actively use this advantage. Although it is still difficult to make objective comparisons, according to Evgeny Nikitin, Russia may well become a leader in the real implementation and real benefits from AI systems.

The interest of medical institutions themselves in implementing AI in their practice At the start of the Moscow experiment, only a third of doctors supported the use of AI. A year later, this figure rose to 60-70% of the number of doctors surveyed by the organizers of the experiment. Obviously, the systems are getting better, and doctors and the management of medical organizations are noticing this.

Commercial clinics are also interested. Previously, the use of AI was seen as a marketing gimmick, but now the situation has changed dramatically.

The interest of medical institutions in introducing AI into their practices

At the start of the Moscow experiment, only a third of doctors supported the use of AI. A year later, this figure rose to 60-70% of the number of doctors surveyed by the organizers of the experiment. Obviously, the systems are getting better, and doctors and the management of medical organizations are noticing this.

Commercial clinics are also interested. Previously, the use of AI was seen as a marketing gimmick, but now the situation has changed dramatically.

Predictions regarding the use of AI in Russian medicine over the next three years

Based on the existing achievements, Evgeny Nikitin made a forecast on the practical application of AI in Russian medical institutions:

The first real success stories in the application of computer vision systems. Studies will be conducted that will prove the economic efficiency of implementation. The use of AI will no longer be considered a hype gimmick or a strange gimmick;
The number of tasks in which the use of AI will be tested will grow by leaps and bounds, but then the situation will stabilize, and systems will be used only where it is economically justified;
The first complex (aimed at finding not a specific disease, but a whole spectrum) and multimodal (e.g., based on analysis of not only images but also medical records and patient tests, as well as data from wearable devices) systems will emerge;
AI systems will be more actively used for routine tasks (calculations, assessment of the quality of the study performed);
The number of high-quality, standardized medical data sets will grow;
Creating a digital ecosystem and unifying data formats can help create a comprehensive ecosystem and roadmap for healthcare - optimizing patient care, triaging patients, planning, and operations. For example, the AI system, by analyzing a CT or MRI scan, will not only be able to diagnose the development of the disease, but also calculate the extent of the involvement of neighboring organs by the disease (invasion) and give a specific recommendation to the surgeon on resection and areas of surgery.

Evgeny Nikitin, head of the ML-development department at Medical Screening Systems, assesses the development of AI solutions in Russian medicine and their prospects this way:

«At the moment, the introduction and use of AI systems in medicine is experimental in nature - there are no real tangible benefits to speak of yet, in my opinion. Nevertheless, only through testing in combat conditions will we be able, firstly, to determine the set of tasks for which the use of machine learning is justified, and, secondly, to bring them to the necessary level of quality. So, we have to be patient a little longer, and most importantly, we have to promote active cooperation between developers and the medical community.».

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Photo by the Press Service of the Mayor and Government of Moscow

AI helps fight cancer in Russia

What results has the Skolkovo resident achieved

At the end of 2021, the company signed an agreement with the Ministry of Health of Uzbekistan to introduce an AI-assisted radiology medical image processing system to the country's clinics.

«Cels» is actively involved not only in the development of technology but also in the development of new standards and regulations.

AI helps in the fight against coronavirus

Photo: Alexander Avilov / AGN Moskva

AI in medicine helps to cut costs

The costs of developing and operating AI systems are still high. This has also been affected by the pandemic, which has led to higher salaries for ML and AI specialists because of the indiscriminate move of all companies to telecommuting-it is now possible to work from anywhere in the world for any company in the world;
The semiconductor crisis (graphics processors went up in price);
Medical tasks are much more complex than in other areas of AI applications. This factor affects the cost of their implementation - more experienced specialists, experiments, data, and computer resources are needed.

How will patients benefit from the use of AI?

Russian citizens in the near future will be able to feel the benefits of the introduction of AI in medicine themselves:

There will be less time for patients to receive diagnosis information by automating routines and reducing the physician's workload;
The quality of diagnosis and treatment will improve;
The patient will undergo fewer tests and procedures;
In the long term, healthcare will become cheaper.

How the field of MedTech is developing in Russia relative to the rest of the world with the use of AI

Commercial clinics are also interested. Previously, the use of AI was seen as a marketing gimmick, but now the situation has changed dramatically.

The interest of medical institutions in introducing AI into their practices

Commercial clinics are also interested. Previously, the use of AI was seen as a marketing gimmick, but now the situation has changed dramatically.

Predictions regarding the use of AI in Russian medicine over the next three years

Based on the existing achievements, Evgeny Nikitin made a forecast on the practical application of AI in Russian medical institutions:

The first real success stories in the application of computer vision systems. Studies will be conducted that will prove the economic efficiency of implementation. The use of AI will no longer be considered a hype gimmick or a strange gimmick;
The number of tasks in which the use of AI will be tested will grow by leaps and bounds, but then the situation will stabilize, and systems will be used only where it is economically justified;
The first complex (aimed at finding not a specific disease, but a whole spectrum) and multimodal (e.g., based on analysis of not only images but also medical records and patient tests, as well as data from wearable devices) systems will emerge;
AI systems will be more actively used for routine tasks (calculations, assessment of the quality of the study performed);
The number of high-quality, standardized medical data sets will grow;
Creating a digital ecosystem and unifying data formats can help create a comprehensive ecosystem and roadmap for healthcare - optimizing patient care, triaging patients, planning, and operations. For example, the AI system, by analyzing a CT or MRI scan, will not only be able to diagnose the development of the disease, but also calculate the extent of the involvement of neighboring organs by the disease (invasion) and give a specific recommendation to the surgeon on resection and areas of surgery.

Evgeny Nikitin, head of the ML-development department at Medical Screening Systems, assesses the development of AI solutions in Russian medicine and their prospects this way:

One of the key goals of the AI strategy is to ensure the growth of welfare and quality of life of the country's population. This goal is impossible without the introduction of AI into the social infrastructure of cities. It includes smart building design, robotic food delivery, self-driving cabs, unified mobile services for a full range of government, medical and social services. All this is the reality of today, and digitalization is only gaining momentum.

What other tasks need to be solved for AI to become a full-fledged tool of city management?

Denis Kolokolnikov, Managing Partner of RRG (consultants for projects and cities of the future on the principles of sustainable development) notes the following:

In order for AI to become a full-fledged tool for managing the urban environment, you need to digitize almost all urban processes. Artificial intelligence is based on big data, both accumulated and online. To apply it effectively and use neural networks, you need a lot of digital information, which should be in a single format which must be processed.

Big data should be systematized so that AI could quickly study it, self-train, build neural networks, make predictions and manage the city.

Moscow - the flagship city of digitalization

In 2017, Moscow was included in the list of the best "digital" cities in the world and it took 7th place. As part of the Moscow 2030 program, the city has advanced even further and entered the top 5 digitalization leaders.

One of the principles of Moscow's smart city is the use of AI for urban tasks like:

The use of self-driving vehicles as utility vehicles that clean up the city without human involvement;
Using AI as a "second opinion" for diagnosis and detection of early stages of disease;
Automatically filling out documents by the city's MFC employees to optimize their routine tasks and increase the number of applications processed per working day.

Marina Lashkevich, an expert from BuyBuyHouse, an IT real estate startup, notes the following challenges of the city's digitalization today - structuring, collecting data sets, and optimizing the interaction between the population and the executive authorities.

«If electronic medical cards, remote receipt of services, and documents from the MFC are available today, the Unified Real Estate Registry is the task of tomorrow. According to Rosreestr's plans, it should be launched in 2023. Such a system of analyzing data arrays of real estate objects will not only perform a supervisory function but will also help realtors and developers to better perform their services, offering the public a truly relevant product».

Plans for the future

Issues of digitalization of the urban environment are often discussed on federal and state portals. Thus, in an article on the information resource of the State Budgetary Institution «MOSSTROYINFORM», experts shared their vision of the digital future.

Murad Damirov, Co-founder and Managing Partner of the BusinessClub network of service offices from Capital Group, noted that the office sector will also join digitalization. He believes that the current system of control and accounting of personnel actions (ACS) will be universally replaced by the facial recognition system.

The expert also believes that in the near future AI can be used to optimize the climatic conditions of offices and apartments - to regulate humidity, temperature, and lighting without human intervention.

The CEO of Global HiPer IT, Arseniy Tarasov is convinced that the main purpose of creating a smart city is the transition from the «post factum» approach (solving a problem) to preventive work with the problem. This is especially relevant in medicine, where with the help of AI it will be possible to recognize complex diseases at the first symptoms, therefore starting treatment faster and saving human lives.

Arseny Tarasov noted that by collecting and analyzing large amounts of data over time, it is possible to create a digital model of the «normal behavior» of an object.

«This approach makes it possible to detect potential problems at the first symptoms - instead of today's traditional parameter monitoring in a BMS. If patterns are disturbed, the system instantly gives a signal. By applying artificial intelligence algorithms, the object is trained to self-regulate: to understand when all is well, to know what to do when the problem is known, and how to call for help if there is no solution.»

What's yet to come

AI is making its way into every home, making life practical and convenient. Perhaps in a few years, we'll spend minimal time doing paperwork, shopping for groceries, and getting around town. However, the city has some serious work to do to become a winner in the digital technology race.

Denis Kolokolnikov notes that for comprehensive digitalization of the city, it is important to work on the optimization of transport infrastructure:

«It is necessary to develop a master plan for the city development, to adopt an actual master plan that would take into account all these shortcomings, trends, taking into account the cluster division, traffic, traffic jams, etc. And only then you can properly design the infrastructure, new jobs, create new centers.

The solution can be a digital twin city - a prototype in which all buildings, roads, engineering communications, all transport, pedestrian, and traffic flows will be digitized and will exist in one model. Then it will work.»

The expert also highlights such urgent tasks as the problem of ecology - garbage collection and disposal, the widespread introduction of AI-based construction management technologies, and the further development of social infrastructure.

«The future belongs to such cities, in which the life expectancy of people is increasing, and for this, they need quality air, at least. This is what the digital city should be based on.»

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What other tasks need to be solved for AI to become a full-fledged tool of city management?

Denis Kolokolnikov, Managing Partner of RRG (consultants for projects and cities of the future on the principles of sustainable development) notes the following:

Big data should be systematized so that AI could quickly study it, self-train, build neural networks, make predictions and manage the city.

Moscow - the flagship city of digitalization

One of the principles of Moscow's smart city is the use of AI for urban tasks like:

The use of self-driving vehicles as utility vehicles that clean up the city without human involvement;
Using AI as a "second opinion" for diagnosis and detection of early stages of disease;
Automatically filling out documents by the city's MFC employees to optimize their routine tasks and increase the number of applications processed per working day.

Plans for the future

Arseny Tarasov noted that by collecting and analyzing large amounts of data over time, it is possible to create a digital model of the «normal behavior» of an object.

What's yet to come

Denis Kolokolnikov notes that for comprehensive digitalization of the city, it is important to work on the optimization of transport infrastructure:

«The future belongs to such cities, in which the life expectancy of people is increasing, and for this, they need quality air, at least. This is what the digital city should be based on.»

Photo: Ramil Sitdikov/POOL/TASS

«Next year, a super-service «Digital Construction« will appear on the public services portal, allowing you to receive services from different agencies in a single window, necessary for the construction of individual and multi-family residential buildings».

Deputy Chairman of the RF Government, D. N. Chernyshenko.

Relevance of the issue

The urgency of the digital transformation of the construction industry issue was also pointed out by Deputy Prime Minister Marat Khusnullin. He believes that the construction process should be as transparent as possible, and provide an opportunity for managers on the ground to make decisions promptly.

Photo: Sergey Mikheev/RG

«Digitalization of the industry goes in two main directions - in providing state services and in the transition of construction companies to modern IT-technologies».

Deputy Prime Minister, Marat Khusnullin.

Building Information Modeling (BIM) design based on AI

BIM technologies are the engines that can put the construction industry on a sustainable digital track and lead to the fulfillment of planned indicators. An innovative approach to design offers the following benefits:

Elimination of errors in the design phase;
Effective control over construction estimates.

In Moscow, there are already facilities where only BIM modeling has been used in their design. For the city and the supervisory authorities, this approach has a number of advantages, since BIM technologies include not only 3D modeling but also a qualitatively new approach to the formation of registers, regulations, and big data databases.

In simple terms, the process of object supervision by Gosstroynadzor bodies can be carried out online. The availability of a unified information system with data on objects and the possibility of obtaining reports promptly simplifies and accelerates the examination of objects and commissioning of the building.

Photo: iStock

Starting from 2022, the use of BIM-projecting will be a mandatory requirement for construction companies working with the state as a customer. The actions of the regulator are consistently pushing the industry towards the necessary digital transformation.

BIM-modeling with the application of AI-technologies is successfully used both by proven suppliers of foreign software products Revit Dynamo, BricsCAD, and the latest versions of ArchiCAD and by our domestic developers.

Examples of domestic developments

In December 2021, the Moscow Innovation Agency held the investment session for IT startups StartHub Moscow. 70 projects were short-listed for the session, of which 3 won grants from the Venture Capital Development Fund of the capital.

BimAR System startup with a system for optimizing production and improving the quality of structural assembly was among the winners. The grant amounted to 300 000 rubles.

BimAR is a system of digital assembly marking, which synchronizes BIM-projecting (information modeling of buildings), mandatory in Russia for all state customers since 2022, with the actions of workers on construction sites, providing highly accurate data on the status of work carried out online.

It is a digital duplicate of the structural elements of buildings, which automatically controls the design, production, warehousing, logistics, and installation of products from anywhere in the world.

Opinion of expert practitioners

Leading design, construction, and engineering companies have long and successfully used AI technology and see its benefits to optimize work at the construction site. And how are smaller market players doing?

In November Sergei Sobyanin, the Mayor of Moscow, called for the reduction of the number of migrants on construction sites in the capital, in order to attract more Russians, increase productivity, as well as mechanization and industrialization of construction. Maxim Lazovsky, the head of the construction company working in the residential housing segment supported this initiative and noted the role of BIM technologies.

In a conversation with the editors, Maxim told how the pilot implementation of modeling in one of his current projects is being carried out.

Maxim Lazovsky, owner of the Lazovsky House construction company

In November our company approved and developed a new standard for the construction of housing projects in accordance with the previously published standard housing standards of the Ministry of Construction and the mandatory use of BIM modelling in our company.

We use both ArchiCAD and the more innovative Revit Dynamo software. At the moment the head of the engineering design department is doing his master's thesis at Moscow State University of Civil Engineering and Architecture, so we are actively recruiting students for internships. Thus, the company performs an important social function - it trains young professionals in new technologies and creates an image of the construction niche as a high-tech industry.

In general, I am fully satisfied with the implementation of BIM. The communication costs of interaction between builders, designers, architects, and clients in the pilot project decreased by 50%. The client gets the clear realization of his wishes (even in such small things as the placement of outlets, switches, etc.), and we get cost optimization.

The construction industry for many years has been one of the most conservative in the country. Forcing a foreman to pick up a tablet and an architect to give up the years-tested AutoCAD solution was an impossible task. Today's development of AI technology allows us to say with confidence - digital construction is real now!

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Photo: Ramil Sitdikov/POOL/TASS

Deputy Chairman of the RF Government, D. N. Chernyshenko.

Relevance of the issue

Photo: Sergey Mikheev/RG

«Digitalization of the industry goes in two main directions - in providing state services and in the transition of construction companies to modern IT-technologies».

Deputy Prime Minister, Marat Khusnullin.

Building Information Modeling (BIM) design based on AI

Elimination of errors in the design phase;
Effective control over construction estimates.

Photo: iStock

Examples of domestic developments

BimAR System startup with a system for optimizing production and improving the quality of structural assembly was among the winners. The grant amounted to 300 000 rubles.

Opinion of expert practitioners

In a conversation with the editors, Maxim told how the pilot implementation of modeling in one of his current projects is being carried out.

Maxim Lazovsky, owner of the Lazovsky House construction company

On November 27, 2021, the Prime Minister of the Russian Federation, Mikhail Mishustin approved the Transport Strategy until 2030 with a forecast until 2035. According to the document, autonomous transportation will reduce the cost of transportation by 15% and increase the capacity of infrastructure by 10%.

The first stage of implementation of self-driving logistics corridors in Russia will be the launch of self-driving trucks between Moscow and St. Petersburg on the highway M-11 «Neva». The Ministry of Transport will equip this highway with digital infrastructure for the safe movement of self-driving trucks by 2024. After that, the project will cover the Central Ring Road and M-12 highway, Moscow - Yekaterinburg.

The creation of the infrastructure by the state and the development of the relevant regulations will enable private companies to develop this sector of the economy. Nevertheless, domestic companies have already achieved impressive results in the development of self-driving transport. The flagships in this field today are Yandex and Sber, which have special divisions for the development, testing, and implementation of self-driving cars.

Yandex - the leader in the implementation of self-driving technologies

To date, Yandex's self-driving car is able to move independently, without driver intervention, from point A to point B in small towns or parts of large cities. The car observes traffic rules, can identify and bypass obstacles (including other cars and people), align itself in the traffic flow, predict the behavior of other road users, and independently plan a safe route.

By the end of 2021, «Yandex» self-driving cars drove more than 17 million kilometers. Currently, the company's fleet includes 170 self-driving cars based on Toyota Prius and Hyundai Sonata. The first European Yandex cab service has been operating in the city of Innopolis in Tatarstan for 3 years and passengers have made 24,000 trips during this time.

Regulatory framework for the development of drones

Drone technology is a completely new sphere. Federal legislation for them is still being developed, and there is no country in the world that has fully completed the process as yet. Countries, where pilot projects using autonomous transport have already been launched, are the furthest in this field - and Russia is among these countries. This approach makes it possible to collect the necessary statistics, to gain experience in the use of technology in services, answer many questions, and develop federal regulation based on real practice.

Of course, the creation of a legislative framework for self-driving technologies is a process that requires joint work between regulators and developers.

In 2020, Russia passed a law on experimental legal regimes. It will allow companies working in innovative areas to launch such pilot projects. Within the framework of this law, Yandex prepared a program, which spells out the conditions for testing drones. The adoption of this program will allow the company to launch a cab service in Moscow without a driver at the wheel, as well as to remove the test engineer from the cabin in Innopolis, where a driverless cab service with an engineer in the passenger seat has been operating for 3 years.

The demand for self-driving vehicles on the market

Businesses are interested in using such technologies, particularly in the logistics sector. A number of logistics areas are already experiencing personnel shortages, especially long-distance cargo transportation. Passenger transportation and last-mile delivery are also actively developing now and their volumes are growing, and at some point they will also come to the lack of personnel. Autonomous transportation technology, whether it's cabs or robotic couriers, will close this gap.

Results in robot delivery services

Yandex's delivery robots had already made about 70,000 deliveries by the end of 2021, which is about 1,000 orders per week. This delivery format proved to be in demand both in Russia and abroad. This year, the company's robots began delivering food to students at two American universities: Ohio State University and Arizona University. Next year, a pilot project of robotic food delivery from Carrefour supermarkets will start in Dubai. In the city of Innopolis in Tatarstan, all Yandex.Food deliveries are made only by robotic couriers.

Successes of other companies in the field of drone technology

In 2020, self-driving cars from SberAvtoTech (a division of Sber) were taken to the streets of Moscow for testing in a major city. Before that, the company's drones successfully passed tests and received permission to drive on public roads.

In 2021, the company introduced a fully autonomous electric car, FLIP, which can also use gas and hydrogen as fuel. The car does not have the usual controls, nor does it have a driver's seat. Thanks to this solution, the autonomous electric car can accommodate 6 people in the cabin. The car is equipped with voice control and Sber entertainment services. The electric car's battery can be changed in 5 minutes.

KAMAZ has been testing its own drone since 2020. The company's cargo drone, designed to transport components, has been tested at the plant for a year. However, already in 2024, the manufacturer plans to put its self-driving trucks on public roads.

At the end of September 2021, Gazprom Neft announced that it was using a fully autonomous GAZelle NEXT electric vehicle, which travels independently on the in-field roads of the Yuzhno-Priobskoye field in the Khanty-Mansi Autonomous District. One dispatcher is enough to control 10 -15 such self-driving vehicles. According to the company's representatives, due to the fact that self-driving vehicles can work around the clock, the efficiency of logistics schemes will increase.

The role of Russian companies in the development of self-driving transportation in the world

Russia is one of the leaders in the nascent market of self-driving cars. One of the most important criteria for technology development is the readiness to launch self-driving cab services. «Yandex» is one of 4 companies in the world that performs cab service rides without a person at the wheel.

Moreover, the company's technology is easily adaptable to different environments and can be exported abroad - the company's cars are already in operation in the U.S. and Israel.

Olga Rudakova of Yandex's drone technology press service sees the near future of self-driving transport in Russia in this way:

Yandex technology already allows cars to drive in fully autonomous mode on public roads with not too much traffic. As the technology develops, self-driving cars will gain experience and become better at predicting the behavior of other road users in increasingly difficult road conditions. The areas where cars can drive without a safety engineer in the cabin will gradually expand. At first, they will expand to parts of large cities, and in 3-4 years, self-driving cars will be able to operate in the most difficult conditions - the centers of megacities during rush hour. If the necessary laws to regulate the field of self-driving vehicles are passed within the next 3-4 years, the use of this technology could become widespread.

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Yandex - the leader in the implementation of self-driving technologies

Regulatory framework for the development of drones

Of course, the creation of a legislative framework for self-driving technologies is a process that requires joint work between regulators and developers.

The demand for self-driving vehicles on the market

Results in robot delivery services

Successes of other companies in the field of drone technology

The role of Russian companies in the development of self-driving transportation in the world

Moreover, the company's technology is easily adaptable to different environments and can be exported abroad - the company's cars are already in operation in the U.S. and Israel.

Olga Rudakova of Yandex's drone technology press service sees the near future of self-driving transport in Russia in this way:

To solve these problems, The Center for Training Leaders and Digital Transformation Teams was established at the Graduate School of Management, of the Russian Academy of National Economy and Public Administration, in February 2019 under the auspices of the Ministry of Digital Development, Communications and Mass Media, and the Ministry of Economic Development of the Russian Federation.

Ksenia Tkacheva, the director of The Center for Training Leaders and Digital Transformation Teams at GSOMS RANEPA, told the editorial board how artificial intelligence is used in training programs for civil servants and what role it is assigned to.

«The main task of the center is to train state and municipal employees responsible for digital transformation and development of Russian authorities. As well as training specialists who are able to make ethical, quick and informed decisions in the digital economy».

Training programs for civil servants

The Center for Training Leaders and Digital Transformation Teams at the Graduate School of Management of the Russian Academy of National Economy and Public Administration has the following training programs:

«Head of Digital Transformation» — professional retraining for deputy federal ministers and heads/deputies of services and agencies.
«Implementation of digital transformation projects» —advanced training for heads of digital transformation of regional executive authorities (civil servants appointed to be responsible for the digital transformation of federal executive bodies) and participants in project offices for the digital development of federal executive bodies.
«Digital Transformation and Digital Economy: Technologies and Competencies» — advanced training for employees at the regional and municipal levels.
«Fundamentals of Digital Transformation» — advanced training for civil servants at the level of deputy department head and above.

In addition, the center launched open educational courses on the basics of digital transformation on the Stepik online platform.

In all the programs, methodologists and experts focus on the topic of end-to-end technologies, particularly in artificial intelligence, which allows government services to be more accessible and convenient for citizens.

Key areas of training for civil servants in end-to-end technologies

Important areas of training for civil servants are:

Customer-centricity

The beneficiaries of digital transformation should be citizens and businesses. It is to meet their needs that new systems based on artificial intelligence are created.
Information security

Creating services based on artificial intelligence requires access to large amounts of data about citizens. This creates new risks to consider and can have an impact on the level of trust in digital transformation and the government as a whole.

«Public servants are learning to foresee all possible consequences and to broadcast what risks and how exactly they will be addressed».

Cybersecurity is one of the topics that civil servants focus on first: working through information security issues first, then developing a new technology or service.
Ethics

Civil servants are trained in a decision-making algorithm that helps take into account all ethical risks even at the decision-making stage.

«The risks that exist in the public sector are no different from those of general AI systems. The question is their scale. Millions of people may pay the price for possible mistakes. That is why digital transformation leaders at the federal and regional levels have a new responsibility to prevent such problems.

It is important for civil servants to understand end-to-end technologies and know how to work with data, as be able to make ethical decisions based on them».

Ethics of AI in the work of the Center for Leadership and Digital Transformation Teams

The center conducts expert and analytical work in the field of ethics of modern technology. Training in the ethical principles of artificial intelligence applications, among other things, is based on reports co-authored by the Center with leading experts in the field: «Ethics and the Digital: Ethical Issues in Technology» (2020) and «Ethics and Digital: From Problems to Solutions» (2021).

The center's research papers on ethics were reviewed at the «Ethics of Artificial Intelligence: the Beginning of Trust» conference, where experts from the center and co-authors led a session entitled «The Ethics of Artificial Intelligence in the State».

Participants of the session raised questions of new responsibilities that fall on heads of state IT services, possible risks of discrimination of citizens during implementation of artificial intelligence solutions in state services, global practices and standards of international interaction on regulation of artificial intelligence, and cases of ethical decisions in the state sector.

«The state as a regulator must first and foremost look out for the interests of citizens, making sure that technology is ethical, not discriminatory. In 2020-2021, states and companies are collecting data in quantities that previously would have unequivocally looked like a gross violation of privacy. On the other hand, technology offers too many opportunities for the state to ignore. It is important to make sure that these opportunities are not used to the detriment of citizens," said session moderator - Ekaterina Potapova, Head of Research and Analytics at the Center for Leadership and Digital Transformation Training at the Graduate School of Management, of the Russian Academy of National Economy and Public Administration.

This approach to training based on the principles of client centricity, security and ethics helps civil servants to provide quality services based on artificial intelligence.

Some of the projects are developed by civil servants during the training. Later on, these services are developed and implemented at the regional or federal level. As an example, the project «Digital Transformation of Control and Supervisory Activities in the Agro-industrial Sector - Digital Agricultural Supervision», which was developed by students of the program «Digital Transformation Leader» in 2019. The model of using artificial intelligence was implemented in FGIS «Vetis» and the module «Mercury».

This year, at a meeting of the attestation commission chaired by Deputy Prime Minister Dmitry Chernyshenko, the team of digital transformation leaders presented the service based on artificial intelligence «Smart Cadastre».

The project was presented by Elena Martynova, Deputy Head of Rosreestr. The service, based on pre-trained neural networks and spatial analysis algorithms, helps protect citizens' property interests and put unused real estate into circulation. The service was tested as part of the experiment on the creation of the Unified Information Resource on Land and Real Estate, which is being carried out in 2021 in four pilot subjects of the Russian Federation: Republic of Tatarstan, Perm and Krasnodar territories, Irkutsk region.

Other projects developed by civil servants trained under the center's programs include: the portal and mobile application «Thermal Points of the Emergency Ministry», where artificial intelligence technologies are used to predict the spread of forest fires in large areas; a robot for rapid diagnosis of colds in pre-school educational institutions; and an automated system for assigning social support measures «Service without an official», developed by the KhMAO digital transformation team.

In conclusion, Ksenia Tkacheva highlighted the special role of artificial intelligence in solving the strategic task of transitioning to a digital economy:

«The transition to a digital economy is impossible without the use of artificial intelligence technologies in the social and economic spheres, in public administration. That is why training specialists in creating services based on end-to-end technologies and predicting the consequences of implementation and development of these developments is one of the main tasks of personnel training.»

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Training programs for civil servants

«Head of Digital Transformation» — professional retraining for deputy federal ministers and heads/deputies of services and agencies.
«Implementation of digital transformation projects» —advanced training for heads of digital transformation of regional executive authorities (civil servants appointed to be responsible for the digital transformation of federal executive bodies) and participants in project offices for the digital development of federal executive bodies.
«Digital Transformation and Digital Economy: Technologies and Competencies» — advanced training for employees at the regional and municipal levels.
«Fundamentals of Digital Transformation» — advanced training for civil servants at the level of deputy department head and above.

In addition, the center launched open educational courses on the basics of digital transformation on the Stepik online platform.

Key areas of training for civil servants in end-to-end technologies

Important areas of training for civil servants are:

Customer-centricity

The beneficiaries of digital transformation should be citizens and businesses. It is to meet their needs that new systems based on artificial intelligence are created.
Information security

Creating services based on artificial intelligence requires access to large amounts of data about citizens. This creates new risks to consider and can have an impact on the level of trust in digital transformation and the government as a whole.

«Public servants are learning to foresee all possible consequences and to broadcast what risks and how exactly they will be addressed».

Cybersecurity is one of the topics that civil servants focus on first: working through information security issues first, then developing a new technology or service.
Ethics

Civil servants are trained in a decision-making algorithm that helps take into account all ethical risks even at the decision-making stage.

«The risks that exist in the public sector are no different from those of general AI systems. The question is their scale. Millions of people may pay the price for possible mistakes. That is why digital transformation leaders at the federal and regional levels have a new responsibility to prevent such problems.

It is important for civil servants to understand end-to-end technologies and know how to work with data, as be able to make ethical decisions based on them».

Ethics of AI in the work of the Center for Leadership and Digital Transformation Teams

This approach to training based on the principles of client centricity, security and ethics helps civil servants to provide quality services based on artificial intelligence.

In conclusion, Ksenia Tkacheva highlighted the special role of artificial intelligence in solving the strategic task of transitioning to a digital economy:

In 2020, Moscow Emergency Medical Care Station A.S. Puchkov of The Healthcare Department of Moscow launched a platform for prompt patient care, which handles incoming calls with the help of AI. According to the Head Doctor of the station, the AI helped reduce the speed of the arrival of the ambulance team to the patient and improve the quality of medical care.

Deputy Prime Minister Dmitry Chernyshenko noted that thanks to AI, the Ministry of Health was able to launch the 112 system in 2020, which during the pandemic helped to promptly handle calls from citizens with COVID-19 symptoms.

New AI-based technologies are also being implemented in other government services, but they are most widely used by commercial organizations, including small businesses.

Alexander Dujnikov, co-founder of the Russian company Marketcall, which brings together advertisers and webmasters on its platform, explains with his example how AI works in call processing and the benefits it comes with.

Marketcall has long been actively using a neural network to analyze calls from potential customers and detect fraud. But this is far from being the only way to apply high technology. In the process of creating additional services, a transcribing and robocall functionality was developed and used to increase the efficiency of the call center. In the process of creating them, the company delved into developing voice features and even created its own bot capable of providing initial advice to potential customers.

How AI is cutting costs in call processing

AI systems in call centers are proving to of positive importance by covering most basic customer contact cases, such as processing orders from a limited catalog or providing information on common topics.

However, there is a limited scope of the application when it comes to recognizing user intentions that are not related to any specific topic. An example would be a conversation on a loose topic, where the AI must adequately answer any question and clearly understand what the user wants. At the current stage of technology development, no one has accomplished such a task yet. Creating such an AI would mean creating an artificial intelligence capable of thinking like a human, because every phrase may have a hidden context that only a human can grasp.

Another area that needs further work is speech recognition. With the limited quality of telephone connections, noise, the presence of many accents, and other factors that degrade voice flow, clearly recognizing the spoken phrase is a very difficult task. However, a call center can significantly improve its performance, with existing technology.

Alexander Dujnikov, the co-founder of Marketcall, notes the results achieved with AI:

«AI-assisted call processing has reduced call center downtime while waiting for the target client, and managers don't have to talk to clients who have limited information about the company. This at first seemed to be small but it yielded a tangible financial result - we were able to almost cut our payroll by half, as fewer employees are now required to work with huge bases».

Results of robocall usage

Robocalls are already being widely used in practice, but there are some challenges in this direction. The generated voice is still very different from a live human voice, and it is almost obvious to the consumer that it’s a robot calling. People have not yet adhered to such calls and they do not always react adequately.

However, there are areas where robocalls show excellent results. Tests on notifying customers about the delivery of goods and confirming order placement have shown that a robot can fully replace a live operator today.

AI in call analysis

AI can be seen as a kind of superstructure on top of operators' work, providing additional meta-information on a call. Fraud detection is a special case of such a superstructure. In a general sense, AI can analyze a call and identify features that are specific to it, comparing it to millions of other calls, which is quite problematic for a human to do. AI can also identify hidden indicators that an operator might miss.

AI can recognize sentiment and hidden meaning by analyzing tens or hundreds of thousands of similar calls and finding small triggers that a human might miss. For example, AI can determine when a caller is in the mood to consider offers and make the offer at the most opportune moment, similar to psychologists using covert NLP tactics.

AI can perform statistical analysis of calls and provide detailed reporting on a huge sample in a matter of seconds. A department of «live» employees can do the same in weeks or even months. Also, AI will reduce the processing time of the typical appeals from users, without wasting the time of «real» employees on routine.

Photo: Official website of the Mayor of Moscow

When using AI, virtual operators can be connected to the line, which means the customer will get an answer even if there are no «live» operators available. Basic requests are already set up to be answered by AI - this trend is clearly visible in banking, telecommunications, and logistics. For example, in 2019, Tinkoff Bank launched the voice assistant «Oleg», which can transfer money, provide help, and deactivate services.

Theoretically, it is already possible to build a call center without live operators at all. In addition, unlike a human operator, the AI will always answer a question correctly, without forgetting important details or mixing up information. This can also be popular especially in e-commerce, where presales are very important when communicating with an operator.

Those companies that do not have the technical capability to develop their own AI-based systems can buy ready-made solutions. For example, MegaFon started selling a cloud-based technology, Intelligent Call Processing, that calls customers and receives calls while learning in the process.

Immediate prospects in AI call processing

Right now, AI is being actively implemented only in large companies with large customer flows. But Alexander Dujnikov expresses confidence that with time and more active technology development, the use of AI in call processing will become mainstream and most services, stores, and other businesses will find it difficult to do without it:

«In the coming years, the use of AI-assisted call-handling technology is expected to boom. Already now many large companies use such technologies, without even hinting at the fact. In the future, we will see the widespread use of automation at all levels, from voice assistants for buying tickets in cinemas to personal assistants in phones that can greatly facilitate the daily routine and plan the workday».

[~DETAIL_TEXT] =>

New AI-based technologies are also being implemented in other government services, but they are most widely used by commercial organizations, including small businesses.

How AI is cutting costs in call processing

Alexander Dujnikov, the co-founder of Marketcall, notes the results achieved with AI:

Results of robocall usage

AI in call analysis

Photo: Official website of the Mayor of Moscow

Immediate prospects in AI call processing

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AI helps fight cancer in Russia

What results has the Skolkovo resident achieved

AI helps in the fight against coronavirus

AI in medicine helps to cut costs

How will patients benefit from the use of AI?

How the field of MedTech is developing in Russia relative to the rest of the world with the use of AI

The interest of medical institutions in introducing AI into their practices

Predictions regarding the use of AI in Russian medicine over the next three years

Evgeny Nikitin, head of the ML-development department at Medical Screening Systems, assesses the development of AI solutions in Russian medicine and their prospects this way:

AI helps fight cancer in Russia

What results has the Skolkovo resident achieved

AI helps in the fight against coronavirus

AI in medicine helps to cut costs

How will patients benefit from the use of AI?

How the field of MedTech is developing in Russia relative to the rest of the world with the use of AI

The interest of medical institutions in introducing AI into their practices

Predictions regarding the use of AI in Russian medicine over the next three years

Evgeny Nikitin, head of the ML-development department at Medical Screening Systems, assesses the development of AI solutions in Russian medicine and their prospects this way:

Denis Kolokolnikov, Managing Partner of RRG (consultants for projects and cities of the future on the principles of sustainable development) notes the following:

Moscow - the flagship city of digitalization

Plans for the future

What's yet to come

Denis Kolokolnikov notes that for comprehensive digitalization of the city, it is important to work on the optimization of transport infrastructure:

Denis Kolokolnikov, Managing Partner of RRG (consultants for projects and cities of the future on the principles of sustainable development) notes the following:

Moscow - the flagship city of digitalization

Plans for the future

What's yet to come

Denis Kolokolnikov notes that for comprehensive digitalization of the city, it is important to work on the optimization of transport infrastructure:

Deputy Chairman of the RF Government, D. N. Chernyshenko.

Relevance of the issue

Deputy Prime Minister, Marat Khusnullin.

Building Information Modeling (BIM) design based on AI

Examples of domestic developments

Opinion of expert practitioners

Maxim Lazovsky, owner of the Lazovsky House construction company

Deputy Chairman of the RF Government, D. N. Chernyshenko.

Relevance of the issue

Deputy Prime Minister, Marat Khusnullin.

Building Information Modeling (BIM) design based on AI

Examples of domestic developments

Opinion of expert practitioners

Maxim Lazovsky, owner of the Lazovsky House construction company

Yandex - the leader in the implementation of self-driving technologies

Regulatory framework for the development of drones

The demand for self-driving vehicles on the market

Results in robot delivery services

Successes of other companies in the field of drone technology

The role of Russian companies in the development of self-driving transportation in the world

Olga Rudakova of Yandex's drone technology press service sees the near future of self-driving transport in Russia in this way:

Yandex - the leader in the implementation of self-driving technologies

Regulatory framework for the development of drones

The demand for self-driving vehicles on the market

Results in robot delivery services

Successes of other companies in the field of drone technology

The role of Russian companies in the development of self-driving transportation in the world

Olga Rudakova of Yandex's drone technology press service sees the near future of self-driving transport in Russia in this way:

Training programs for civil servants

Key areas of training for civil servants in end-to-end technologies

Ethics of AI in the work of the Center for Leadership and Digital Transformation Teams

Training programs for civil servants

Key areas of training for civil servants in end-to-end technologies

Ethics of AI in the work of the Center for Leadership and Digital Transformation Teams

How AI is cutting costs in call processing

Alexander Dujnikov, the co-founder of Marketcall, notes the results achieved with AI:

Results of robocall usage

AI in call analysis

Immediate prospects in AI call processing

How AI is cutting costs in call processing

Alexander Dujnikov, the co-founder of Marketcall, notes the results achieved with AI:

Results of robocall usage

AI in call analysis

Immediate prospects in AI call processing

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