Prospects for the implementation of artificial intelligence and computer vision technologies in laboratory medicine (literature review)

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Abstract

Laboratory diagnostics plays one of the leading roles in modern medicine, providing doctors of clinical specialties with data for timely diagnosis, selection of tactics and methods of treatment. To ensure high efficiency and increase the accuracy of research, artificial intelligence technologies have recently been actively introduced into the practice of the laboratory service: computer vision, machine learning, deep learning, neural networks, data bank analysis. In laboratory diagnostics, these technologies are successfully used to automate and improve technological processes, including processing reaction results, cytomorphological images, and analysis of the obtained data. One of the promising areas for the implementation of artificial intelligence in laboratory diagnostics is the development of technologies for phenotyping blood groups using widely used monoclonal antibodies as reagents and computer vision technology on wearable devices. At the same time, there are often no ready-made solutions on the market for including intelligent software systems in the daily work of the laboratory. The review considers various examples of the use of technological systems based on artificial intelligence in laboratory diagnostics. The paper also presents a bibliometric analysis of scientific literature on the spread of computer vision, machine learning, and artificial intelligence technologies in medical laboratories based on publications from the Pubmed database over the past 20 years. In addition, the review discusses the prospects and limitations of using artificial intelligence and computer vision in medical laboratories and assesses the benefits of introducing the blood group phenotyping method into clinical practice using artificial intelligence technology on mobile devices.Contribution of the authors: Tregub P.P. — research concept and design, writing the text, compiling of the list of literature, statistical data processing;Zhemchugin D.E., Zubanov P.S. — writing the text, compiling of the list of literature, editing; Goldberg A.S., Godkov M.A., Akimkin V.G. — writing the text, editing. All authors are responsible for the integrity of all parts of the manuscript and approval of the manuscript final version.Acknowledgment. The study had no sponsorship.Conflict of interest. The authors declare no conflict of interest.Received: February 21, 2025 / Accepted: March 11, 2025 / Published: April 30, 2025

About the authors

Pavel P. Tregub

Central Research Institute of Epidemiology; First Moscow State Medical University named after I.M. Sechenov (Sechenov University; Scientific Center of Neurology

Email: tregub@cmd.su

Dmitry E. Zhemchugin

Municipal Clinical Hospital named after M.P. Konchalovsky; Moscow Regional Research Clinical Institute named after M.F. Vladimirsky

Email: Dmitriy_Zh@mail.ru

Pavel S. Zubanov

Central Research Institute of Epidemiology

Email: zubanov@cmd.su

Arkady S. Goldberg

Russian Medical Academy of Continuous Professional Education

Email: goldarcadiy@gmail.com

Mikhail A. Godkov

Russian Medical Academy of Continuous Professional Education; N.V. Sklifosovsky Research Institute for Emergency Medicine of the Moscow City Health Department

Email: mgodkov@yandex.ru

Vasily G. Akimkin

Central Research Institute of Epidemiology

Email: vgakimkin@yandex.ru

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