Russian Journal of Infection and ImmunityRussian Journal of Infection and ImmunityИнфекция и иммунитет2220-76192313-7398SPb RAACI1800310.15789/2220-7619-VAI-18003REVIEWSОБЗОРЫUnknownVALIDATED ARTIFICIAL INTELLIGENCE APPLICATIONS IN PANDEMIC PREPAREDNESS: A FOCUSED SCOPING REVIEW OF PEER-REVIEWED, EXPLICITLY VALIDATED STUDIESВАЛИДИРОВАННЫЕ ПРИЛОЖЕНИЯ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ДЛЯ ОБЕСПЕЧЕНИЯ ГОТОВНОСТИ К ПАНДЕМИИ: ЦЕЛЕВОЙ ОБЗОР РЕЦЕНЗИРОВАННЫХ И ДОСТОВЕРНЫХ ПРОВЕРЕННЫХ ИССЛЕДОВАНИЙhttps://orcid.org/0000-0003-0085-3284EskandarKirolosЭскандарКиролос
Egypt

Physician Researcher;

Academic Degree: M.D.;

affiliation: Helwan University, Faculty of Medicine and Surgery, Egypt;

врач-исследователь;

Ученая степень: доктор медицинских наук;

аффилиация: Университет Хелуана, факультет медицины и хирургии, Египет;

kiroloss.eskandar@gmail.comHelwan University, Giza, EgyptУниверситет Хелуана, Гиза, Египет031220251009202505112025Copyright ©; , Eskandar K.Copyright ©; , Eskandar K.Eskandar K.Eskandar K.https://creativecommons.org/licenses/by/4.0https://iimmun.ru/iimm/article/view/18003

Background: Artificial intelligence (AI) has been widely explored during the COVID-19 pandemic, yet the robustness and real-world readiness of such applications remain unclear. Understanding domain-specific strengths and gaps is essential for guiding future investment and policy. Unlike prior syntheses that included preprints or studies without explicit validation, this review restricted inclusion to peer-reviewed studies reporting internal, external, or prospective validation, thereby providing a higher-fidelity assessment of AI readiness.

Aim: To synthesise and critically appraise peer-reviewed AI applications that met explicit validation criteria for pandemic preparedness and response, highlighting methodological strengths, limitations, and priority areas for advancement.

Methodology: Following PRISMA-ScR guidelines, a systematic search was conducted in MEDLINE, Scopus, Web of Science, and IEEE Xplore from inception to June 30, 2024, supplemented by reference screening. Inclusion was restricted to full-text, peer-reviewed empirical studies applying AI or machine learning to pandemic preparedness, detection, response, or recovery in human populations, and reporting either (i) validated quantitative performance (internal, external, or prospective) or (ii) measurable real-world/simulated impact. Screening, data extraction, and risk-of-bias assessment were performed independently and in duplicate using a PROBAST-adapted tool to ensure methodological rigour.

Results: Sixteen studies met inclusion criteria, spanning diagnostic imaging (n = 5), clinical outcome prediction (n = 1), surveillance and epidemiological modelling (n = 3), digital contact tracing (n = 3), and single studies in digital health review, PPE supply-chain optimisation, Long-COVID biomarkers, and mental-health surveillance. External validation was more frequent in imaging studies than in other domains but still not universal; only three studies included prospective, real-world evaluation. Most datasets originated from high-income settings. Compared with earlier broad reviews that included non-validated models, these findings reveal fewer but more methodologically sound AI applications with tangible readiness for translation.

Conclusion: While select AI applications—notably diagnostic imaging and large-scale epidemiological models—demonstrate promising validated performance, prospective real-world evaluations and geographically diverse datasets remain scarce. Funders and journals should prioritise external and prospective validation, transparent reporting (TRIPOD-AI/CONSORT-AI), and open code/data sharing to enable reliable deployment. Strategic investment in rigorously validated, context-appropriate AI solutions—including underexplored areas such as supply-chain optimisation, mental-health surveillance, and biomarker discovery—could strengthen global health-emergency preparedness.

Введение: Искусственный интеллект (ИИ) широко изучался во время пандемии COVID-19, однако надежность и готовность подобных приложений к применению в реальных условиях остаются неясными. Понимание сильных и слабых сторон ИИ крайне важно для определения будущих инвестиций и политики. В отличие от предыдущих исследований, включавших препринты или исследования без полной валидации, настоящий обзор приводит рецензируемые исследования, сообщающими о внутренней, внешней или перспективной валидации, предоставляя более высокую точность оценки готовности к внедрению ИИ.

Цель: Обобщить и критически оценить рецензируемые приложения ИИ, соответствующие полным критериям валидации для обеспечения готовности к пандемии и реагирования на нее, с оценкой методологических сильных и слабых сторон, а также приоритетных направлений развития.

Методология: В соответствии с рекомендациями PRISMA-ScR был проведен систематический поиск в базах данных MEDLINE, Scopus, Web of Science и IEEE Xplore с момента создания по 30 июня 2024 года, дополненный скринингом референтных источников. Были изучены полнотекстовые рецензируемые эмпирические исследования по применению ИИ или машинного обучения для обеспечения готовности к пандемии, ее выявления, реагирования или выздоровления в популяциях людей, и сообщающими либо (i) о подтвержденных количественных показателях (внутренних, внешних или проспективных), либо (ii) об измеримом воздействии в реальных условиях/моделировании. Скрининг, извлечение данных и оценка риска смещения проводились независимо в двух повторах с использованием адаптированного к PROBAST инструмента для обеспечения методологической строгости.

Результаты: Шестнадцать исследований соответствовали критериям включения, охватывая диагностическую визуализацию (n = 5), прогнозирование клинических исходов (n = 1), эпиднадзор и эпидемиологическое моделирование (n = 3), электронное эпидемиологическое расследование (n = 3) и отдельные исследования, посвященные обзору цифрового здравоохранения, оптимизации цепочки поставок СИЗ, биомаркерам пост-COVID-19 синдрома и эпиднадзору за психическим здоровьем. Внешняя валидация в исследованиях визуализации встречалась чаще, чем в других областях, но не всегда; только три исследования включали проспективную оценку в реальных условиях. Большинство наборов данных были получены из регионов с высоким уровнем дохода. По сравнению с предыдущими масштабными обзорами, включавшими невалидированные модели, приводимые результаты выявили меньшее количество, но более методологически обоснованных приложений ИИ с готовностью к внедрению.

Заключение: Хотя отдельные приложения ИИ, в частности, диагностическая визуализация и крупномасштабные эпидемиологические модели, демонстрируют многообещающие подтвержденные результаты, отмечается недостаток в проспективных оценках в реальных условиях и наличие географически-локализованных баз данных. Спонсорам и научным журналам следует отдавать приоритет внешней и перспективной валидации, прозрачной отчетности (TRIPOD-AI/CONSORT-AI) и открытому обмену кодом/данными для обеспечения надежного применения. Стратегические инвестиции в тщательно проверенные, соответствующие контексту решения на основе ИИ, включая малоизученные области, такие как оптимизация цепочек поставок, эпиднадзор за психическим здоровьем и поиск биомаркеров, могут повысить глобальную готовность к чрезвычайным ситуациям в области здравоохранения.

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