Aug 2026· Journal of Psychopathology and Clinical Science· 2 citations
Medicine
TL;DR
The Evidence-Based Psychotherapy with AI (EBP-AI) framework is introduced, which articulates a set of principles for developing effective clinical AI applications and introduces a set of key technical questions for the development and evaluation of clinical LLMs and AIs aligned with these principles.
Abstract
Artificial intelligence (AI) systems and large language models (LLMs) offer substantial potential to augment or even fundamentally change elements of psychological assessment and treatment. However, current AI technologies have yet to demonstrate the capacity to effect meaningful and sustained clinical change. This gap reflects both the limited integration of clinical science knowledge into language models and applications built using them, as well as the mismatch between the brief, minutes-long nature of most AI interactions and the months-long course of most evidence-based treatments. Here we introduce the Evidence-Based Psychotherapy with AI (EBP-AI) framework, which articulates a set of principles for developing effective clinical AI applications: a) psychodiagnostic assessment, b) longitudinal case conceptualization, c) appropriately dosed intervention planning, d) meaningful progress evaluation, e) rigorous validation with clinical populations, f) attention to real world implementation and use, g) clinically appropriate style, and h) understanding clinical psychology as a living science. We introduce a set of key technical questions for the development and evaluation of clinical LLMs and AIs aligned with these principles. Despite their potential, current clinical AIs fall short, in part due to issues with memory, sycophancy, and prioritizing short-term helpfulness over long-term clinical impact. Responsible and ethical design of effective, clinical-science-based AI systems will require understanding their limitations and strategically extending their capabilities.
FIT is a proposed framework, designed to reduce three specific risks that have been linked to conversational MH-AI, and the dimensions are intended as practical starting guardrails, not as an exhaustive list.
J. Steinberg, W. Thies, John R. Weisz· Journal of Consulting and Cl...· 0 citations
Five priorities define a translational agenda for 2026 and beyond: AI in mental health will succeed not through model performance alone, but through disciplined integration into clinical workflows, measurement systems, and governance structures that ensure safety, equity, and real-world effectiveness.
Martin P. Paulus, J. Torous, R. Perlis et al.· NPP—Digital Psychiatry and N...· 0 citations
This protocol provides a reproducible foundation for developing clinically relevant conversational AI in occupational therapy and for future feasibility and effectiveness studies.
Pantelis Pergantis, N. Bardis, Charalabos Skianis et al.· Brazilian Journal of Science· 1 citation
Artificial intelligence (AI) is transforming mental health care by enabling novel approaches to assessment, outcome prediction, and treatment. However, the rapid growth of AI tools has outpaced evidence synthesis on their realworld clinical value, implementation barriers, and ethical risks, leaving clinicians and policymakers without clear guidance for responsible integration. This narrative review aimed to examine current AI applications in mental health care, identify their clinical benefits, implementation barriers, and ethical challenges, and define conditions for responsible integration that preserve the patient-clinician relationship. A structured literature search of PubMed, PsycINFO, and Scopus was conducted for peer reviewed English language articles published between January 2019 and March 2026. Studies addressing AI applications, benefits, implementation, or ethics in mental health were included and synthesised narratively into thematic categories. The review found that AI tools—for example, selfreferral chatbots that reduced waiting times and increased treatment uptake—provide expanded 24/7 access, improved clinical efficiency, and potential for individualised personalisation. Predictive models showed promise for treatment selection and risk stratification, while natural language processing unlocked unstructured clinical data. However, concerns included patient data privacy, algorithmic bias that may worsen existing inequities, potential erosion of the therapeutic relationship, and mixed acceptance among clinicians and patients. Many AI applications remain experimental, and regulatory frameworks have not kept pace with technological developments. This review did not include formal quality appraisal or quantitative synthesis; the evidence is limited by short followup periods, high dropout in chatbot trials, and a predominance of studies from highincome countries. Future research should employ longitudinal, codesigned, mixedmethods designs and pragmatic trials that evaluate clinical outcomes alongside equity, user trust, and the preservation of empathic, humanled care—rather than relying solely on uncontrolled implementation studies. AI should be responsibly integrated to augment, not replace, the clinical workforce. Successful application requires a balance between technological advancement, patient protection, and preservation of the patient-clinician relationship.
Shizal Nawaz, Laiba Nawaz, Hasnain Ali et al.· Digital Medicine· 0 citations
Abstract Background AI has become increasingly used in mental health care for applications such as diagnosis, monitoring, and treatment support. These include tools like clinician support systems, large language models, and conversational agents used to augment psychotherapy and clinical decision-making. While prior research suggests potential benefits of and concerns with AI, little is known within the domain of obsessive-compulsive disorder (OCD). Given the expanding role of AI in psychiatry, understanding these perspectives is essential to ensuring AI implementation aligns with patient priorities and values. Objective This study aims to explore the perspectives of individuals with OCD on the use of AI in health care, including perceived benefits, risks, and its role in relation to human clinicians. Methods We conducted semistructured interviews with 24 adults self-reporting OCD, recruited through online communities and advocacy networks. Eligible individuals (≥18 y with self-reported OCD) completed screening, provided informed consent, and participated in remote Health Insurance Portability and Accountability Act (HIPAA)-compliant Zoom (Zoom Communications, Inc) interviews (May-December 2024). Transcripts were deidentified, open-coded, and used to develop a codebook. Focused codes were applied using a thematic analysis framework in Dedoose (v9.2.22; Sociocultural Research Consultants, LLC). Each transcript was independently coded by 2 reviewers, with discrepancies resolved through consensus. Themes were developed through iterative interpretive analysis of code clusters. Results Participants’ perspectives encompassed concerns and benefits of AI in mental health care. Participants expressed concerns about the accuracy and efficacy of information provided by AI, as well as a limited ability for clinical judgment in psychiatric care. Additionally, participants emphasized the importance of human connection, particularly therapeutic alliance, empathy, and reassurance provided by clinicians, which they felt AI could not replicate. Concerns about data privacy, security, and downstream use of information were also highlighted. Despite concerns, many endorsed the use of AI as an adjunct rather than a replacement for clinicians, noting potential benefits in symptom monitoring, preliminary information gathering, and support for administrative tasks, provided that human oversight is maintained. Conclusions Individuals with OCD expressed nuanced views on AI in mental health care, balancing cautious optimism with several concerns. While AI may improve efficiency, standardization, and symptom monitoring, participants highlighted risks related to deindividualization, accuracy, and erosion of human connection. These findings underscore the importance of patient-centered, ethically guided AI integration that preserves the therapeutic alliance while leveraging technological benefits.
Daniel Mokhtar, Harris Wang, E. Garland et al.· Journal of Participatory Med...· 0 citations
Based on the findings, AI seems highly promising for patient-specific ASD therapy via proactive, data-driven scaffolding, but more RCTs and crucial augmentation of representation gaps concerning adult and female ASD phenotype studies are required.
Julia Kuca, Magdalena Stencel, Błażej Pilarski et al.· Frontiers in Psychiatry· 0 citations
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