Evidence map›Paper›PMID 42872200›Full record

ArticleCognitive and behavioral practice2025

TherapyTrainer: Using AI to Train Therapists in Written Exposure Therapy.

Elizabeth C Stade, Johannes C Eichstaedt, Debra L Kaysen, Aadesh Salesha, Alanna Greenberger, Shreya Singhvi, Shannon Wiltsey Stirman

Abstract read
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In one paragraph

Article in Cognitive and behavioral practice, 2025. 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

7 authors.

Elizabeth C StadeInstitute of Human-Centered Artificial Intelligence, Stanford University, Palo Alto, California.
Johannes C EichstaedtInstitute of Human-Centered Artificial Intelligence, Stanford University, Palo Alto, California.
Debra L KaysenDepartment of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, California.
Aadesh SaleshaDepartment of Computer Science, Stanford University, Palo Alto, California.
Alanna GreenbergerDepartment of Psychology, Palo Alto University, Palo Alto, California.
Shreya SinghviInstitute of Human-Centered Artificial Intelligence, Stanford University, Palo Alto, California.
Shannon Wiltsey StirmanDepartment of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, California.

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
NIMH NIH HHS P50 MH139450
6 · The paper itself

Abstract

Though evidence-based treatments for mental disorders are effective, existing implementation efforts are expensive and difficult to scale. Novel solutions- especially those that offer active learning strategies, repeat skill practice and personalized feedback to therapists - are needed to fill this gap. We developed TherapyTrainer, which uses large language models (LLMs) to allow therapists to practice delivering written exposure therapy (WET) for PTSD to AI-Patients while receiving expert feedback from an AI-Consultant. Here we present initial feasibility, acceptability and usability data for TherapyTrainer gathered from therapists, supervisors, and WET expert-consultants across iterative rounds of development. In Phase 1, we rapidly prototyped and developed TherapyTrainer based on ongoing feedback from WET clinicians and experts (

Indexed as

artificial intelligencedisseminationevidence-based treatmentslarge language modelsPTSDwritten exposure therapy

Identifiers

PMID42872200
PMCPMC13660795

What OpenQuestion holds

Textmetadata
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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.