Evidence map›Paper›PMID 42748037›Full record

ArticleJMIR mental health2026

Psychological Therapy in the Age of Large Language Models: Framework for Therapist-Delivered and AI-Supported Functions.

Shane Cross, Nickolai Titov, Blake Dear, John Gleeson, Mario Alvarez-Jimenez

Abstract read
In one paragraph

Article in JMIR mental health, 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.

Shane CrossOrygen, 35 Poplar Rd, Parkville, Melbourne, Victoria, 3052, Australia, 61 3 9966 9383.ORCID http://orcid.org/0000-0002-5413-8342
Nickolai TitovSchool of Psychological Sciences, Macquarie University, Sydney, New South Wales, Australia.ORCID http://orcid.org/0000-0002-7268-729X
Blake DearSchool of Psychological Sciences, Macquarie University, Sydney, New South Wales, Australia.ORCID http://orcid.org/0000-0001-9324-3092
John GleesonHealthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Melbourne, Victoria, Australia.ORCID http://orcid.org/0000-0001-7969-492X
Mario Alvarez-JimenezOrygen, 35 Poplar Rd, Parkville, Melbourne, Victoria, 3052, Australia, 61 3 9966 9383.ORCID http://orcid.org/0000-0002-3535-9086

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: Large language models are increasingly used within and alongside therapy. As large language models perform more therapy functions, questions arise about what the future might hold for therapists and what they will do. We argue that the enduring therapist role in the age of AI-assisted care is currently best understood through relational, adaptive, and accountability functions. These functions include therapeutic challenge, use of the therapeutic relationship as a mechanism of change, rupture detection and repair, bearing witness to suffering, calibration of pace and treatment burden, and clinical judgment under uncertainty across the broader care pathway. Drawing on psychotherapy theory, digital mental health research, the declarative-procedural-reflective model by Bennett-Levy, and our clinical experience, we propose a clinically informed, hypothesis-generating, relational-adaptive-accountability framework. This framework is intended to support further empirical testing and may have implications for workforce development, supervision, training, and service design.

Indexed as

Artificial IntelligencePsychotherapyHumansLarge Language ModelsAIartificial intelligencedigital mental healthlarge language modelsmental health servicespsychotherapysupervisiontherapist competencetrainingworkforce development

Identifiers

PMID42748037
PMCPMC13580604

What OpenQuestion 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 OpenQuestion graph.