Evidence map›Paper›PMID 42621241›Full record

SynthesisFrontiers in psychology2026

AI-powered cognitive remediation for schizophrenia: a psychologist's lens on evidence and scalability in India.

Drishti Gehlot, Sheetal Musale

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in psychology, 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

2 authors.

Drishti GehlotDepartment of Psychology, School of Vedic Sciences, MIT Art, Design and Technology University, Pune, India.
Sheetal MusaleDepartment of Psychology, School of Vedic Sciences, MIT Art, Design and Technology University, Pune, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Schizophrenia spectrum disorders (SSDs) are marked by cognitive deficits, functional impairment, and notable treatment gap in India, with lifetime prevalence of 1.41%, current prevalence as 0.42%, and the gap of 72%, added by a severe workforce gap, that is, 0.75 psychiatrists per 100,000 and 0.29 clinical psychologists per 100,000. Scalable tools are required by the psychologists that can fill the substantial urban-rural gap. Aim: To evaluate how effective, feasible, and clinically applicable AI-powered cognitive remediation is, emphasizing India/LMIC contexts. Methods: PRISMA-guided umbrella review with narrative synthesis of records published between 2020-2026, identified 48 studies (14 randomized controlled trials (RCTs), 22 reviews, 8 pilots, and 4 bibliometric studies) across PubMed, Scopus, Web of Science. Cognition, functioning, and feasibility, retention, and usability were included as outcome measures. Results: AI-based AI-powered cognitive remediation showed generally favorable effects. Reported effect sizes ranged from moderate to large across different interventions and outcomes. VR-based social-cognition training showed largest benefits and smartphone-based programs showed promise for working memory and engagement. Hybrid or monitored formats showed generally better retention and usability than fully self-directed interventions. However, the evidence remained heterogeneous, and many findings came from small studies, feasibility work, or reviews of mixed quality. Conclusion: AI-powered CRT appears to be a promising model for schizophrenia rehabilitation when delivered through psychologist-monitored hybrid models. Such approaches may improve access, support rural and semi-rural service delivery, and help reduce the treatment gap through scalable, culturally adaptable, and technology-enabled care, especially in rural India.

Indexed as

artificial intelligencecognitive remediationlow- and middle-income countries (LMICs)schizophreniasmartphone interventionsvirtual reality

Identifiers

PMID42621241
PMCPMC13487369

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.