Deciphering the Mosaic of Therapeutic Potential: A Scoping Review of Neural Network Applications in Psychotherapy Enhancements
Bibliographic record
Abstract
Background: Psychotherapy is a component of the therapeutic options accessible in mental health. Along with psychotherapy techniques and indications, there is a body of studies on what are known as psychotherapy’s common factors. However, up to 40% of patients do not respond to therapy. Artificial intelligence approaches are hoped to enhance this and with the growing body of evidence of the use of neural networks (NNs) in other areas of medicine, this domain is lacking in the field of psychotherapy. This study aims to identify the different uses of NNs in the field of psychotherapy. Methods: A scoping review was conducted in the electronic databases EMBASE, MEDLINE, APA, and CINAHL. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement influenced this study’s design. Studies were included if they applied a neural network algorithm in the context of a psychotherapeutic approach. Results: A total of 157 studies were screened for eligibility, of which 32 were fully assessed. Finally, eight articles were analyzed, and three uses were identified: predicting the therapeutic outcomes, content analysis, and automated categorization of psychotherapeutic interactions. Conclusions: Uses of NNs were identified with limited evidence of their effects. The potential implications of these uses could assist the therapist in providing a more personalized therapeutic approach to their patients. Given the paucity of literature, this study provides a path for future research to better understand the efficacy of such uses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".