Healthcare (Basel, Switzerland) · 2026
Conversational AI in Cognitive and Social Training for People with Dementia: A Systematic Review.
СтатьяОбзорНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Chan Mark K K, Ng Peter H F, Liu Karen P Y
Аннотация
BACKGROUND: Conversational artificial intelligence (AI), including text-based chatbots, voice-based agents, multimodal systems, and socially assistive robots (SARs), offers a scalable adjunct to therapist-led dementia care. The post-2022 emergence of large language models (LLMs) has accelerated development, yet few reviews apply a unified conversational AI taxonomy across dementia care. This review synthesized the effectiveness, limitations, and implementation challenges of conversational AI across the dementia care continuum.
METHODS: Six databases (PubMed, Embase, Web of Science, Scopus, IEEE Xplore, ACM Digital Library) were searched for English-language studies (January 2010-March 2026) evaluating conversational AI targeting cognitive, social, or caregiver outcomes. Two reviewers independently screened and extracted data following PRISMA 2020 guidelines; risk of bias used standard tools and findings were synthesized narratively.
PROTOCOL: PROSPERO CRD420261333625.
RESULTS: Forty studies (8 randomized controlled trials [RCTs], 32 non-randomized) were included. SARs were the largest category (n = 24; 60.0%), followed by text-based chatbots (n = 12; 30.0%), multimodal systems (n = 3; 7.5%), and voice-based chatbots (n = 1; 2.5%). The strongest cognitive evidence came from a social robot RCT (gain of 3.9 points on a 30-point screening measure (p < 0.001). For caregivers, an international RCT (n = 274) showed significant reductions in depression (d = 0.37) and burden (d = 0.34). LLM-based systems produced an 18-fold increase in conversation duration. Speech recognition failure was the most consistently reported technical barrier.
CONCLUSIONS: Conversational AI shows directional benefit across cognitive, social, and caregiver outcomes. Critical research gaps remain regarding voice-only randomized evidence and adequately powered LLM trials against usual care.
METHODS: Six databases (PubMed, Embase, Web of Science, Scopus, IEEE Xplore, ACM Digital Library) were searched for English-language studies (January 2010-March 2026) evaluating conversational AI targeting cognitive, social, or caregiver outcomes. Two reviewers independently screened and extracted data following PRISMA 2020 guidelines; risk of bias used standard tools and findings were synthesized narratively.
PROTOCOL: PROSPERO CRD420261333625.
RESULTS: Forty studies (8 randomized controlled trials [RCTs], 32 non-randomized) were included. SARs were the largest category (n = 24; 60.0%), followed by text-based chatbots (n = 12; 30.0%), multimodal systems (n = 3; 7.5%), and voice-based chatbots (n = 1; 2.5%). The strongest cognitive evidence came from a social robot RCT (gain of 3.9 points on a 30-point screening measure (p < 0.001). For caregivers, an international RCT (n = 274) showed significant reductions in depression (d = 0.37) and burden (d = 0.34). LLM-based systems produced an 18-fold increase in conversation duration. Speech recognition failure was the most consistently reported technical barrier.
CONCLUSIONS: Conversational AI shows directional benefit across cognitive, social, and caregiver outcomes. Critical research gaps remain regarding voice-only randomized evidence and adequately powered LLM trials against usual care.