Evidence map›Paper›PMID 42512622›Full record

ReviewHealthcare (Basel, Switzerland)2026

Conversational AI in Cognitive and Social Training for People with Dementia: A Systematic Review.

Mark K K Chan, Peter H F Ng, Karen P Y Liu

Abstract readReview
In one paragraph

Review in Healthcare (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mark K K ChanDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Peter H F NgDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China.ORCID 0000-0002-9671-896X
Karen P Y LiuDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China.ORCID 0000-0001-7397-5149

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundConversational 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.

methodsSix 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.

resultsForty studies (8 randomized controlled trials [RCTs], 32 non-randomized) were included. SARs were the largest category (

conclusionsConversational 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.

Indexed as

chatbotcognitive trainingconversational AIdementialarge language modelmild cognitive impairmentsocially assistive robotsystematic review

Identifiers

PMID42512622
PMCPMC13410334

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.