Evidence map›Paper›PMID 42211192›Full record

ArticleFrontiers in psychiatry2026

Conversational AI for perinatal mental health: promise, limits, and a human-AI stepped-care framework.

Haiyan Yang, Dong-Mei Lin

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 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.

Haiyan YangReproductive Medicine Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Dong-Mei LinThird Affiliated Hospital of Wenzhou Medical University (Ruian People's Hospital), Wenzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Perinatal mental health conditions are common yet persistently underdetected and undertreated. Recent advances in generative artificial intelligence (AI) and multimodal interaction offer new opportunities for scalable, continuous support, making the concept of the "digital doula" particularly compelling. Because perinatal care relies heavily on emotional support, repeated contact, and timely escalation, conversational AI is attractive as a potential adjunct to existing care. We argue that digital doulas should not be framed as autonomous substitutes for clinicians or human doulas, but rather conceptualized as AI-enabled relational interfaces embedded within a stepped-care model. We propose four core functions for digital doulas: companionship, symptom interpretation, navigation, and sentinel monitoring. These roles can reduce disclosure barriers, extend support between clinical visits, and strengthen linkage to appropriate care. However, conversational AI also introduces significant risks, including the illusion of empathy, crisis recognition failures, algorithmic bias against vulnerable populations, data privacy vulnerabilities, and unsafe deployment. To address these risks, we outline a safety-by-design agenda emphasizing human oversight, clear escalation protocols with medico-legal accountability, workflow integration, and equity-centered evaluation. Ultimately, the clinical value of digital doulas depends on safely strengthening the perinatal mental health care cascade without replacing its core human relationships.

Indexed as

conversational AIdigital doulagenerative AIperinatal mental healthpostpartum depressionstepped care

Identifiers

PMID42211192
PMCPMC13212461

What OpenQuestion holds

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LicenceCC BY
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.