urmjУральский медицинский журналUral Medical Journal2949-4389Ural State Medical University10.52420/umj.24.6.120AXSNEOurmj-2033Research ArticleЛитературные обзоры | Literature reviewsLiterature reviewsВозможности использования нейросетей в судебной медицинеApplications of Neural Networks in Forensic Medicinehttps://orcid.org/0000-0002-3709-1546ДолговаО. Б.DolgovaO. B.

Оксана Борисовна Долгова – доктор медицинских наук, доцент, заведующий кафедрой патологической анатомии и судебной медицины, институт клинической медицины

Екатеринбург

Oksana B. Dolgova – Doctor of Sciences (Medicine), Associate Professor, Head of the Department of Pathological Anatomy and Forensic Medicine, Institute of Clinical Medicine

Ekaterinburg

obdolgova@gmail.comhttps://orcid.org/0009-0009-7232-5473ЯкимоваЮ. Г.YakimovaYu. G.

Юлия Геннадьевна Якимова – ассистент кафедры патологической анатомии и судебной медицины,институт клинической медицины

Екатеринбург

Yulia G. Yakimova – Assistant of the Department of Pathological Anatomy and Forensic Medicine, Institute of Clinical Medicine

Ekaterinburg

yakimova_juli@mail.ruhttps://orcid.org/0009-0004-5862-9693СайлерП. А.SaylerP. A.

Полина Александровна Сайлер – ординатор кафедры патологической анатомии и судебной медицины, институт клинической медицины

Екатеринбург

Polina A. Sayler – Resident of the Department of Pathological Anatomy and Forensic Medicine, Institute of Clinical Medicine

Ekaterinburg

polia.chugaeva@yandex.ruhttps://orcid.org/0009-0008-8143-1043КондрашовД. Л.KondrashovD. L.

Дмитрий Львович Кондрашов – кандидат медицинских наук, доцент, доцент кафедры патологической анатомии и судебной медицины, институт клинической медицины

Екатеринбург

Dmitry L. Kondrashov – Candidate of Sciences (Medicine), Associate Professor, Associate Professor of the Department of Pathological Anatomy and Forensic Medicine, Institute of Clinical Medicine

Ekaterinburg

kdl@uralsudmed.ruhttps://orcid.org/0009-0003-2358-1223Шабунина-БасокН. Р.Shabunina-BasokN. R.

Наталья Рудольфовна Шабунина-Басок – доктор медицинских наук, профессор, профессор кафедры патологической анатомии и судебной медицины, институт клинической медицины

Екатеринбург

Natalya R. Shabunina-Basok – Doctor of Sciences (Medicine), Professor, Professor of the Department of Pathological Anatomy and Forensic Medicine, Institute of Clinical Medicine

Ekaterinburg

bassokmax@mail.ruУральский государственный медицинский университетРоссияUral State Medical UniversityRussian Federation202523122025246120–135Copyright © Долгова О.Б., Якимова Ю.Г., Сайлер П.А., Кондрашов Д.Л., Шабунина-Басок Н.Р., 20252025Долгова О.Б., Якимова Ю.Г., Сайлер П.А., Кондрашов Д.Л., Шабунина-Басок Н.Р.Dolgova O.B., Yakimova Y.G., Sayler P.A., Kondrashov D.L., Shabunina-Basok N.R.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.umjusmu.ru/jour/article/view/2033Введение

Введение. Современные вызовы, связанные с ростом объема данных в различных сферах человеческой деятельности, требуют внедрения инновационных методов анализа, среди которых особое место занимает искусственный интеллект (ИИ). В медицине технологии машинного обучения активно применяются для автоматизации диагностики, прогнозирования заболеваний и обработки медицинских изображений. Судебно-медицинская экспертиза также начинает использовать нейросетевые алгоритмы для повышения точности и скорости исследований.

Цель работы – изучить современные подходы к использованию ИИ в судебно-медицинской экспертизе и смежных областях на основании литературных данных за последние 10 лет.

Материалы и методы

Материалы и методы. Анализ и систематизация научных публикаций, размещенных в базах данных PubMed, Scopus, Web of Science, eLibrary.ru, «КиберЛенинка» за 2015–2024 гг. (имеется менее 5 % источников за 2008–2014 гг.) по поисковым словам artificial intelligence, forensic examination, machine learning. Обнаружено 98 источников, по критериям включения отобрано 62.

Результаты и обсуждение

Результаты и обсуждение. Ключевые направления применения нейросетей в судебной медицине включают в себя идентификацию личности, в т. ч. по черепу, зубному статусу и ДНК, анализ повреждений, обработку биометрических данных, статистическую обработку медицинской информации для выявления скрытых закономерностей. Использование компьютерного зрения позволяет автоматизировать анализ фото- и видеоматериалов с мест преступлений, а прогнозные модели на основе ИИ помогают в установлении времени наступления смерти и определении факторов, влияющих на исход судебно-медицинских исследований. Однако внедрение нейросетей в экспертную практику требует решения ряда проблем, включая валидацию алгоритмов, обеспечение достоверности результатов, соблюдение этико-правовых норм.

Заключение

Заключение. Таким образом, применение нейросетей в судебной медицине, хотя обладает потенциалом, сегодня ограничено необходимостью дорогостоящей инфраструктуры, риском алгоритмических предубеждений и отсутствием правовой базы. Внедрение ИИ возможно лишь как часть комплексного исследования при обязательном контроле со стороны эксперта-человека.

Introduction

Introduction. The growing volume of data across diverse fields demands innovative analytical approaches, with artificial intelligence (AI) emerging as a pivotal tool. In forensic medicine, neural networks are increasingly being adopted to improve the precision and efficiency of examinations. However, challenges persist, including algorithm validation, reliability assurance, and adherence to ethical standards.

Aim of work is to examine modern applications of AI in forensic medical examination and related fields, focusing on literature from the past decade (2015–2024).

Materials and methods

Materials and methods. A systematic analysis of scientific publications from PubMed, Scopus, Web of Science, eLibrary.ru, CyberLeninka was conducted, prioritizing recent studies (2015–2024), with less than 5 % of sources dating back to 2008–2014.

Results and discussion

Results and discussion. Key applications of neural networks in forensic medicine include: individual identification (via skull, dental records, and DNA), injury analysis (mechanism, timing, and cause of death), biometric data processing (facial recognition, skull-based facial reconstruction), and statistical analysis of medical data to uncover hidden patterns. Despite their potential, limitations such as the need for robust validation, legal compliance, and ethical considerations hinder widespread adoption.

Conclusion

Conclusion. AI technologies show significant promise in improving the speed and precision of forensic examinations. However, further research is needed to address existing challenges and ensure their reliable integration into expert practice.

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