Технологии искусственного интеллекта в психиатрии: оценка потенциала и барьеров в диагностике депрессивных состояний. Систематический обзор

Введение . В условиях возрастающего уровня тревожности особое значение приобретает внедрение цифровых технологий с акцентом на искусственный интеллект как ключевых инструментов, способствующих улучшению ситуации путем повышения эффективности диагностики и контроля лечения ментальных заболеваний. Цель исследования : систематизация данных о современном состоянии, перспективах и ограничениях применимости ИИ-систем в рамках диагностики психических расстройств, в первую очередь, депрессивных состояний, и оценка зрелости существующих технологий с целью их внедрения в реальную клиническую практику. Материалы и методы . Поиск литературы проводился в базе данных PubMed (MEDLINE). В систематический обзор включены 74 оригинальные статьи, опубликованные на английском языке в период с января 2021 по март 2026 г. Использовались тематические блоки поисковых запросов, ориентированные на выявление публикаций, исследующих возможности диагностики депрессии с помощью анализа речи, голоса, акустики, выражения лица и компьютерного зрения. Статьи подверглись многоэтапному отбору: исключение повторов, предварительный экспресс-анализ по названиям и аннотациям, подробное изучение избранных публикаций. Результаты . Настоящее исследование продемонстрировало перспективность применения искусственного интеллекта, особенно мультимодальных подходов, в области выявления психических расстройств, в том числе депрессивных состояний. Вместе с тем был обнаружен ряд критических ограничений, препятствующих широкому клиническому внедрению данной технологии. К ним относятся дефицит исследований, выполненных в условиях реальной клинической практики, потенциальная предвзятость алгоритмов в отношении различных половозрастных и этнических групп, сложность интерпретации рекомендаций искусственного интеллекта и недостаток прямых сравнений с профессиональной оценкой квалифицированных специалистов. Необходимо также учитывать географическую концентрацию исследований в азиатском регионе, что подчеркивает важность проведения дальнейших исследований с более репрезентативными выборками с целью обеспечения обобщаемости результатов. Заключение . Перспективным направлением представляется разработка гибридных моделей, интегрирующих возможности искусственного интеллекта с опытом и знаниями клиницистов. Успешное решение этих задач позволит реализовать потенциал искусственного интеллекта для раннего выявления заболеваний, персонализации терапии и улучшения качества жизни пациентов с депрессией.

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