Актуальность

vitj

Врач и информационные технологии

Medical Doctor and Information Technologies

1811-01932413-5208

Pirogov National Medical and Surgical Center

10.25881/18110193_2026_1_22

vitj-310

Research Article

ОБЗОРЫ

REVIEWS

Аспекты применения искусственного интеллекта для скрининга и диагностики заболеваний: нарративный обзор

Aspects of the use of artificial intelligence for disease screening and diagnosis: a narrative review

Гаранин

А. А.

Garanin

A. A.

к.м.н.

г. Самара

PhD

Samara

sameagle@yandex.ru

Рубаненко

О. А.

Rubanenko

O. A.

д.м.н.

г. Самара

DSc

Samara

olesya.rubanenko@gmail.com

Трусов

Ю. А.

Trusov

Yu. A.

г. Самара

Samara

yu.a.trusov@samsmu.ru

Сенюшкин

Д. В.

Senyushkin

D. V.

г. Самара

Samara

d.v.senushkin@samsmu.ru

Колсанов

А. В.

Kolsanov

A. V.

д.м.н., профессор, член-корреспондент РАН

г. Самара

DSc, Professor, Corresponding Member of the Russian Academy of Sciences

Samara

a.v.kolsanov@samsmu.ru

ФГБОУ ВО «СамГМУ» Минздрава РоссииРоссияFederal State Budgetary Educational Institution of Higher Medical Education "SamSMU" of the Ministry of Health of the Russian FederationRussian Federation

2026

29032026

012237

2026

Гаранин А.А., Рубаненко О.А., Трусов Ю.А., Сенюшкин Д.В., Колсанов А.В.

Garanin A.A., Rubanenko O.A., Trusov Y.A., Senyushkin D.V., Kolsanov A.V.

This work is licensed under a Creative Commons Attribution 4.0 License.

https://www.vit-j.ru/jour/article/view/310

Актуальность

Актуальность. Активное внедрение искусственного интеллекта (ИИ) в сферу здравоохранения существенно повышает эффективность ранней диагностики различных заболеваний. Среди перспективных направлений внедрения ИИ выделяются методы диагностики, основанные на анализе голоса, дистанционном мониторинге кровообращения методом фотоплетизмографии, отслеживании особенностей движения глаз, а также использование умных устройств для непрерывного мониторинга здоровья. Важнейшей задачей является разработка интегрированных систем комплексного медицинского скрининга.

Цель

Цель. Оценка литературных данных и анализ результатов применения искусственного интеллекта для раннего выявления заболеваний.

Материал и методы

Материал и методы. Проведен поиск в базах данных PubMed (Medline), Google Scholar, eLibrary, Web of Science, Scopus, CyberLeninka работ на английском и русском языках, в которых осуществлялся скрининг заболеваний, по ключевым терминам «screening», «diagnostics», «artificial intelligence», «machine learning», «disease», «скрининг», «диагностика», «искусственный интеллект», «заболевание», «машинное обучение», «глубокое обучение». Включение исследований (2015–2025 гг.) основано на независимой оценке тремя исследователями, которые пришли к единому мнению.

Результаты

Результаты. Для данного обзора в процессе отбора соответствующих исследований включена 31 работа, отвечающая критериям поиска, из 1141 публикаций.

Заключение

Заключение. Скрининг и диагностика заболеваний с применением ИИ предоставляет существенную информацию о состоянии пациента, снижая риск человеческого фактора и пропуска ранних признаков заболевания.

Background

Background. The active implementation of artificial intelligence (AI) in healthcare significantly improves the effectiveness of early diagnosis of various diseases. Promising areas for AI implementation include diagnostic methods based on voice analysis, remote blood flow monitoring using photoplethysmography, eye movement tracking, and the use of smart devices for continuous health monitoring. A key objective is the development of integrated systems for comprehensive medical screening.

Aim

Aim. To evaluate the literature and analyze the results of artificial intelligence implementation for the early detection of diseases.

Materials and methods

Materials and methods. A search was conducted in the databases PubMed (Medline), Google Scholar, eLibrary, Web of Science, Scopus, CyberLeninka for English- and Russian-language publications. We used the keywords "screening," "diagnostics," "artificial intelligence," "machine learning," "disease," "screening," "diagnostics," "artificial intelligence," "disease," "machine learning," and "deep learning." Studies from 2015 to 2025 were included based on independent review by three researchers who reached a consensus.

Results

Results. Thirty-one papers from 1,141 publications that met the search criteria were included for this review.

Conclusions

Conclusions. AI-based disease screening and diagnosis provides valuable information about a patient's condition, reducing the risk of human error and missing early signs of disease.

скринингзаболеваниеискусственный интеллектмашинное обучение

screeningdiseaseartificial intelligencemachine learning

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The authors declare that there are no conflicts of interest present.