Conversational AI should fill the white space in mental health care, not replace humans.

Nick Kabrel, Elizabeth C Stade, Shannon Wiltsey Stirman, Jaan Aru, Johannes C Eichstaedt

Abstract readReview

In one paragraph

Review in NPJ digital medicine, 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

5 authors.

Nick KabrelDepartment of Psychology, University of Zurich, Zurich, Switzerland. nick.kabrel@psychologie.uzh.ch.

Elizabeth C StadeDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.

Shannon Wiltsey StirmanDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.

Jaan AruInstitute of Computer Science, University of Tartu, Tartu, Estonia.

Johannes C EichstaedtInstitute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA. johannes.stanford@gmail.com.

Funding

Evaluation of a Multiagent LLM-Based Simulated Patient to Train Therapists in Written Exposure Therapy for PTSDP50MH139450 · NIMH · STANFORD UNIVERSITY · PI SHANNON Wiltsey STIRMAN · 2025 to 2026

$7.0M

Digital Society Initiative Excellence FellowshipEesti Teadusagentuur PSG728NIMH NIH HHS P50 MH139450NIMH P50 MH139450

6 · The paper itself

Abstract

Conversational AI (CAI) is being rapidly deployed in mental healthcare, with much of the focus on chatbots delivering therapy directly to patients. However, this focus overlooks broader opportunities to improve mental healthcare as a system. Rather than replacing clinicians in psychotherapy, CAI may have greater near-term value in parts of the care system where support is limited or absent. We highlight opportunities for CAI in prevention and screening, while on waitlists, between therapy sessions, and after treatment, and identify candidate mechanisms through which CAI could improve care at each stage.

Identifiers

PMID42786199

DOI10.1038/s41746-026-03088-1

PMCPMC13612589

What Socratic holds

Textmetadata

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 Socratic graph.