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A scoping review of machine learning in psychotherapy research
Abstract
Machine learning (ML) offers robust statistical and probabilistic techniques that can help to make sense of large amounts of data. This scoping review paper aims to broadly explore the nature of research activity using ML in the context of psychological talk therapies, highlighting the scope of current methods and considerations for clinical practice and directions for future research. Using a systematic search methodology, fifty-one studies were identified. A narrative synthesis indicates two types of studies, those who developed and tested an ML model (k=44), and those who reported on the feasibility of a particular treatment tool that uses an ML algorithm (k=7). Most model development studies used supervised learning techniques to classify or predict labeled treatment process or outcome data, whereas others used unsupervised techniques to identify clusters in the unlabeled patient or treatment data. Overall, the current applications of ML in psychotherapy research demonstrated a range of possible benefits for indications of treatment process, adherence, therapist skills and treatment response prediction, as well as ways to accelerate research through automated behavioral or linguistic process coding. Given the novelty and potential of this research field, these proof-of-concept studies are encouraging, however, do not necessarily translate to improved clinical practice (yet).
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“Although predictive models show promise for improving healthcare access and quality,23 evaluative studies in applied settings are limited and primarily focus on predicting outcomes in care, not on improving provider-client fit from the start of care.24-26”
From Using Machine Learning to Match Clients and Therapy Providers: Evaluating Clinical Quality and Cost of Care (2025) · cites this paper as [24]
In the citing paper
“AI has begun to be used in psychology, for example, to retrospectively evaluate which factors predict a better response to psychological treatment once it has finished [72,73].”
From Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review (2022) · cites this paper as [72]
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The promise of machine learning in predicting treatment outcomes in psychiatry
Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., Iniesta, R., et al. (2021) · World Psychiatry · doi:10.1002/wps.20882 · Cited by 531
Modern views of machine learning for precision psychiatry
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Metadata
- Authors
- Aafjes-van Doorn, K., Kamsteeg, C., Bate, J., & Aafjes, M.
- Affiliations
- Ferkauf Graduate School of Psychology, Yeshiva University, Bronx, NY, USA
- Deliberate.ai, New York, NY, USA
- Yeshiva University (US)
- Venue
- Psychotherapy Research
- Publisher
- Informa UK Limited
- Published
- 2020-08-29
- Volume
- 31
- Issue
- 1
- Pages
- 92-116
- ISSN
- 1050-3307 (ISSN-L)
- 1468-4381
- OpenAlex
- W3082929259
- Cited by
- 179
- References
- 111
- Citations by year
- 2026: 16 · 2025: 35 · 2024: 43 · 2023: 33 · 2022: 32 · 2021: 18 · 2020: 2
- FWCI
- 8.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychotherapy Research (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- machine learning, supervised learning, unsupervised learning, treatment outcome prediction, medication adherence, therapist skills, automated coding, clinical implementation
- MeSH terms
- Machine Learning, Humans, Psychotherapy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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