Standardization in regulating artificial intelligence systems in Russian healthcare

Artificial intelligence technologies in medical practice are a promising direction in the world. Artificial intelligence medical decision support systems, diagnostic and screening programs can help medical personnel in routine and complex tasks and improve the level of medical care provided to patients. At the same time, the development, production and distribution of artificial intelligence systems must be regulated without fail. Registration and subsequent control (post-registration monitoring) of artificial intelligence systems in medicine require the creation, adjustment of the legal framework and technological regulation. The Russian Federation has developed a promising development strategy in this area. Seven national standards have been developed by experts in the field of Artificial intelligence in healthcare. These standards establish the procedures for conducting clinical and technical trials, performance requirements and the concept of life cycle, a quality management system and risk management. Aseparate standards is devoted to dataset creation for training and testing the developed algorithms, requirements for them and a metadata format. There are plans to bring the developed national standards to the international level, which will allow Russian manufacturers of artificial intelligence systems implemented these national standards to comply with foreign counterparts and become more competitive at the international level. The international community has already supported the development of an ISO standard based on the national standard for clinical trials. The development will be performed based on the technical committee ISO/TC215 (Health informatics) in conjunction with ISO/IEC JTC1/SC42 (Artificial intelligence), this will allow bringing the national requirements for the Artificial intelligence to the international level. The cycle of these standards will summarize recognized methodologies, helping both manufacturers and medical organizations, doctors and patients to produce and use aquality, safe and effective product.

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METHODOLOGICAL BASIS FOR THE DEVELOPMENT STRATEGY OF ARTIFICIAL INTELLIGENCE SYSTEMS IN THE REPUBLIC OF KAZAKHSTAN IN THE MESSAGE OF THE PRESIDENT OF THE REPUBLIC OF KAZAKHSTAN DATED OCTOBER 5, 2018

NEWS of National Academy of Sciences of the Republic of Kazakhstan ◽

10.32014/2018.2518-170x.34 ◽

2018 ◽

Vol 6 (432) ◽

pp. 47-54 ◽

Cited By ~ 1

Author(s):

Kalimoldayev M. N. ◽

◽

Pak I. T. ◽

Baipakbayeva S. T. ◽

Mun G. A. ◽

...

Keyword(s):

Artificial Intelligence ◽

Development Strategy ◽

Methodological Basis ◽

The Development Strategy ◽

The Republic ◽

Artificial Intelligence Systems

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Artificial intelligence as a moving force of improvement and innovative development in education and pedagogic

Informatics and Education ◽

10.32517/0234-0453-2018-33-10-21-30 ◽

2019 ◽

pp. 21-30

Author(s):

M. G. Koliada ◽

T. I. Bugayova

Keyword(s):

Artificial Intelligence ◽

Intelligent Systems ◽

Intelligent System ◽

Interaction Analysis ◽

Educational Data Mining ◽

Main Task ◽

Intellectual System ◽

Will Force ◽

To Come ◽

Artificial Intelligence Systems

The article discusses the history of the development of the problem of using artificial intelligence systems in education and pedagogic. Two directions of its development are shown: “Computational Pedagogic” and “Educational Data Mining”, in which poorly studied aspects of the internal mechanisms of functioning of artificial intelligence systems in this field of activity are revealed. The main task is a problem of interface of a kernel of the system with blocks of pedagogical and thematic databases, as well as with the blocks of pedagogical diagnostics of a student and a teacher. The role of the pedagogical diagnosis as evident reflection of the complex influence of factors and reasons is shown. It provides the intelligent system with operative and reliable information on how various reasons intertwine in the interaction, which of them are dangerous at present, where recession of characteristics of efficiency is planned. All components of the teaching and educational system are subject to diagnosis; without it, it is impossible to own any pedagogical situation optimum. The means in obtaining information about students, as well as the “mechanisms” of work of intelligent systems based on innovative ideas of advanced pedagogical experience in diagnostics of the professionalism of a teacher, are considered. Ways of realization of skill of the teacher on the basis of the ideas developed by the American scientists are shown. Among them, the approaches of researchers D. Rajonz and U. Bronfenbrenner who put at the forefront the teacher’s attitude towards students, their views, intellectual and emotional characteristics are allocated. An assessment of the teacher’s work according to N. Flanders’s system, in the form of the so-called “The Interaction Analysis”, through the mechanism of fixing such elements as: the verbal behavior of the teacher, events at the lesson and their sequence is also proposed. A system for assessing the professionalism of a teacher according to B. O. Smith and M. O. Meux is examined — through the study of the logic of teaching, using logical operations at the lesson. Samples of forms of external communication of the intellectual system with the learning environment are given. It is indicated that the conclusion of the found productive solutions can have the most acceptable and comfortable form both for students and for the teacher in the form of three approaches. The first shows that artificial intelligence in this area can be represented in the form of robotized being in the shape of a person; the second indicates that it is enough to confine oneself only to specially organized input-output systems for targeted transmission of effective methodological recommendations and instructions to both students and teachers; the third demonstrates that life will force one to come up with completely new hybrid forms of interaction between both sides in the form of interactive educational environments, to some extent resembling the educational spaces of virtual reality.

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Buildings electricity consumption modeling using artificial intelligence systems

System analysis in economics – 2018: Proceedings of the V International research and practice conference–biennale (21–23 november 2018) ◽

10.33278/sae-2018.eng.167-170 ◽

2018 ◽

Author(s):

Eugene Shchetinin

Keyword(s):

Artificial Intelligence ◽

Electricity Consumption ◽

Artificial Intelligence Systems

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About digital transformation of the transport industry in Russia

Scientific notes of the Russian academy of entrepreneurship ◽

10.24182/2073-6258-2020-19-3-95-105 ◽

2020 ◽

Vol 19 (3) ◽

pp. 95-105

Author(s):

Natalia V. Vysotskaya ◽

T. V. Kyrbatskaya

Keyword(s):

Artificial Intelligence ◽

Business Model ◽

Data Science ◽

Digital Transformation ◽

Digital Culture ◽

Blockchain Technology ◽

Community Approach ◽

Non Linear ◽

Transport Industry ◽

Artificial Intelligence Systems

The article is devoted to the consideration of the main directions of digital transformation of the transport industry in Russia. It is proposed in the process of digital transformation to integrate the community approach into the company's business model using blockchain technology and methods and results of data science; complement the new digital culture with a digital team and new communities that help management solve business problems; focus the attention of the company's management on its employees and develop those competencies in them that robots and artificial intelligence systems cannot implement: develop algorithmic, computable and non-linear thinking in all employees of the company.

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Copyrightability of Artworks Produced by Creative Robots, Driven by Artificial Intelligence Systems and the Concept of Originality: The Formality - Objective Model

SSRN Electronic Journal ◽

10.2139/ssrn.2943778 ◽

2017 ◽

Cited By ~ 1

Author(s):

Shlomit Yanisky-Ravid ◽

Luis Antonio Velez-Hernandez

Keyword(s):

Artificial Intelligence ◽

Objective Model ◽

Artificial Intelligence Systems

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CONVOLUTIONAL NEURAL NETWORKS, ANALYTICAL ALGORITHMS, AND PERSONALIZED HEALTH CARE: EMBRACING THE MASSIVE DATA ANALYSIS CAPABILITIES OF DEEP LEARNING ARTIFICIAL INTELLIGENCE SYSTEMS TO COMPLEMENT AND IMPROVE MEDICAL SERVICES

American Journal of Medical Research ◽

10.22381/ajmr5220187 ◽

2018 ◽

Vol 5 (2) ◽

pp. 52 ◽

Cited By ~ 1

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Health Care ◽

Deep Learning ◽

Data Analysis ◽

Convolutional Neural Networks ◽

Massive Data ◽

Personalized Health ◽

Personalized Health Care ◽

Artificial Intelligence Systems

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Nonlinear modelling of the seismic response of masonry structures: Calibration strategies

Bulletin of Earthquake Engineering ◽

10.1007/s10518-021-01104-1 ◽

2021 ◽

Author(s):

Antonio Maria D’Altri ◽

Francesco Cannizzaro ◽

Massimo Petracca ◽

Diego Alejandro Talledo

Keyword(s):

Mechanical Properties ◽

Numerical Models ◽

Shear Stiffness ◽

Model Material ◽

National Standards ◽

National Standard ◽

Masonry Structures ◽

Nonlinear Modelling ◽

Various Boundary Conditions ◽

Panel Stiffness

AbstractIn this paper, a simple and practitioners-friendly calibration strategy to consistently link target panel-scale mechanical properties (that can be found in national standards) to model material-scale mechanical properties is presented. Simple masonry panel geometries, with various boundary conditions, are utilized to test numerical models and calibrate their mechanical properties. The calibration is successfully conducted through five different numerical models (most of them available in commercial software packages) suitable for nonlinear modelling of masonry structures, using nonlinear static analyses. Firstly, the panel stiffness calibration is performed, focusing the attention to the shear stiffness. Secondly, the panel strength calibration is conducted for several axial load ratios by attempts using as reference the target panel strength deduced by well-known analytical strength criteria. The results in terms of panel strength for the five different models show that this calibration strategy appears effective in obtaining model properties coherent with Italian National Standard and Eurocode. Open issues remain for the calibration of the post-peak response of masonry panels, which still appears highly conventional in the standards.

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