Email this article Artificial intelligence in clinical physiology: How to improve learning agility
- Authors: Shutov D.V.1, Sharova D.E.1, Abuladze L.R.1, Drozdov D.V.2
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Affiliations:
- Moscow Center for Diagnostics and Telemedicine
- National Medical Research Center of Cardiology
- Issue: Vol 4, No 1 (2023)
- Pages: 81-88
- Section: Correspondence
- URL: https://journals.rcsi.science/DD/article/view/146878
- DOI: https://doi.org/10.17816/DD123559
- ID: 146878
Cite item
Abstract
Clinical physiology involves a complete, comprehensive, multilateral study of the functions of both affected and healthy organs, which allows us to assess the compensatory capabilities of the body.
Artificial intelligence is increasingly being used in medicine, including in clinical physiology. This is facilitated by the increase in computing processing power, development of cloud services and datasets, and numerous scientific articles demonstrating the effectiveness and viability of such intelligent solutions.
Although the approach to medical dataset development is generally similar, there are a number of key features and significant differences in clinical physiology. Artificial intelligence systems in clinical physiology may be effectively trained and applied in practice by following the recommendations in this study.
The national standard of the Russian Federation GOST R 59921.9-2022, which has entered into force, is included in the set of standards “Artificial Intelligence systems in clinical medicine” and establishes additional requirements for data analysis algorithms and test methods of artificial intelligence systems used in the field of clinical physiology. A crucial feature of the created standard is its qualimetric type (i.e., it has a mandatory set of demonstration data).
Russia is one of the first countries to start developing quasi-metric standards worldwide, and 15 industry standards in the field of artificial intelligence (2 of them in medicine) will come into force this year.
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##article.viewOnOriginalSite##About the authors
Dmitry V. Shutov
Moscow Center for Diagnostics and Telemedicine
Author for correspondence.
Email: ShutovDV@zdrav.mos.ru
ORCID iD: 0000-0003-1836-3689
SPIN-code: 9381-2456
MD, Dr. Sci. (Med.)
Russian Federation, MoscowDariya E. Sharova
Moscow Center for Diagnostics and Telemedicine
Email: ShutovDV@zdrav.mos.ru
ORCID iD: 0000-0001-5792-3912
SPIN-code: 1811-7595
Russian Federation, Moscow
Liya R. Abuladze
Moscow Center for Diagnostics and Telemedicine
Email: AbuladzeLR@zdrav.mos.ru
ORCID iD: 0000-0001-6745-1672
SPIN-code: 8640-9989
Junior Research Associate
Russian Federation, MoscowDmitrii V. Drozdov
National Medical Research Center of Cardiology
Email: cardioexp@gmail.com
ORCID iD: 0000-0001-7374-3604
SPIN-code: 2279-9657
MD, Cand. Sci. (Med.)
Russian Federation, MoscowReferences
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