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Review

AI-Assisted Precision Psychotherapy: Toward a Process-Based Framework of Therapeutic Interaction

by
Shlomo Mendlovic
1,2
1
Shalvata Mental Health Center, Hod Hasharon 45100, Israel
2
ALUMOT: Knowledge, Creativity, Leadership in Psychotherapy—The Clalit Program for Psychotherapy, Tel Aviv University, Tel Aviv 69978, Israel
Psychiatry Int. 2026, 7(3), 124; https://doi.org/10.3390/psychiatryint7030124
Submission received: 23 March 2026 / Revised: 11 May 2026 / Accepted: 21 May 2026 / Published: 4 June 2026

Abstract

Psychotherapy is an effective treatment for a wide range of mental disorders, yet treatment outcomes remain highly variable across patients. The emerging field of precision psychotherapy seeks to address this variability by tailoring interventions to the characteristics and needs of individual patients. Recent advances in artificial intelligence (AI) have accelerated this effort by enabling predictive models that identify patients at risk for treatment non-response and support personalized treatment selection. However, most current approaches to precision psychotherapy focus primarily on predicting outcomes or stratifying patients into subgroups, while offering limited tools for supporting clinical decision-making within the therapeutic process itself. This paper proposes that advancing precision psychotherapy may benefit from conceptual frameworks capable of representing therapeutic interactions as they unfold within clinical sessions. Psychotherapy is fundamentally a communicative process in which psychological change is understood to emerge through the evolving dialogue between therapist and patient. To help structure this process, we introduce a dimensional framework that conceptualizes mental activity as organized across five hierarchical dimensions: action, thought, emotion, experience, and being. These dimensions provide a structured representation of the forms in which psychological activity appears in therapeutic dialogue and may allow therapeutic interaction to be described as a sequence of dimensional transitions between therapist and patient. Such a representation could serve as a conceptual basis for the future development of computational tools aimed at analyzing therapeutic dialogue and identifying patterns that may be associated with therapeutic processes such as change, rupture, or shifts in interaction. Integrated with AI-based analysis of psychotherapy sessions, this framework may inform the design of systems intended to support the analysis of therapeutic processes, such as monitoring patterns of interaction or identifying potential shifts in patient experience, while preserving the central role of clinical judgment. By providing a formalized representation of therapeutic dialogue, this approach is intended as a conceptual and methodological step toward the development of more precise, process-informed approaches to psychotherapy.
Keywords: precision psychotherapy; therapeutic process modeling; dimensional model of mental activity; AI-assisted psychotherapy; natural language processing in psychotherapy precision psychotherapy; therapeutic process modeling; dimensional model of mental activity; AI-assisted psychotherapy; natural language processing in psychotherapy

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MDPI and ACS Style

Mendlovic, S. AI-Assisted Precision Psychotherapy: Toward a Process-Based Framework of Therapeutic Interaction. Psychiatry Int. 2026, 7, 124. https://doi.org/10.3390/psychiatryint7030124

AMA Style

Mendlovic S. AI-Assisted Precision Psychotherapy: Toward a Process-Based Framework of Therapeutic Interaction. Psychiatry International. 2026; 7(3):124. https://doi.org/10.3390/psychiatryint7030124

Chicago/Turabian Style

Mendlovic, Shlomo. 2026. "AI-Assisted Precision Psychotherapy: Toward a Process-Based Framework of Therapeutic Interaction" Psychiatry International 7, no. 3: 124. https://doi.org/10.3390/psychiatryint7030124

APA Style

Mendlovic, S. (2026). AI-Assisted Precision Psychotherapy: Toward a Process-Based Framework of Therapeutic Interaction. Psychiatry International, 7(3), 124. https://doi.org/10.3390/psychiatryint7030124

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