Aug 2026· Journal of Consulting and Clinical Psychology· Vol 94 8, pp.
453-455
· 0 citations
Medicine
TL;DR
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.
Abstract
For artificial intelligence (AI) used in conjunction with psychotherapy, clinicians and clients need a principled basis for deciding which tools to engage with, and intervention designers need guidance on building tools that are most likely to be safe and effective. As the evidence base on mental health (MH)-AI safety and efficacy grows, we propose tools that are FIT: focal (bounded in problem focus), intervention-specific (bounded in therapeutic procedures), and titrated (with dose and duration calibrated to match the use case). FIT is a proposed framework, designed to reduce three specific risks that have been linked to conversational MH-AI. The dimensions are intended as practical starting guardrails, not as an exhaustive list. In this viewpoint article, the authors' argument concerns only MH-AI used in routine clinical care-tools prescribed by or used with input for guidance from a clinician. The authors leave aside fully unsupervised consumer use, which raises additional concerns beyond this viewpoint. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Artificial intelligence is increasingly used in mental health screening, symptom monitoring, conversational support, and low-intensity digital interventions. This structured critical narrative review examines the clinical promise and ethical boundaries of artificial intelligence in anxiety care. A targeted search of PubMed/MEDLINE, official publisher platforms, institutional guidance, and reference lists was updated through June25,2026. Sources were assessed across seven analytical domains: technological class
and intended function, anxiety-related applicability, strength of effectiveness and safety evidence, user experience and persuasive design, ethical and professional risk, equity, and human oversight. Evidence indicates that selected conversational interventions can produce small-to-moderate short-term reductions in anxiety symptoms, but findings are heterogeneous and cannot be generalized from bounded, clinically curated systems to unrestricted consumer chatbots. Persistent risks include delayed escalation, automation of empathy, reassurance dependence, privacy concerns, subgroup bias, and unequal digital access. Artificial intelligence is therefore most defensible as a task-specific, clinically delimited support within stepped or blended care, with transparent role definition, version-specific validation, continuous safety monitoring, and accountable human oversight.
Mario Guadalupe López Ayala· Revista LINCE Ciencias Socia...· 0 citations
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.
Elizabeth C. Stade, Philip Held, H. A. Schwartz et al.· Journal of Psychopathology a...· 2 citations
A list of eight best practices was created to assist developers with designing AI systems in a way that would reduce the overall risk of harm for users attempting to use their AI for mental health cases.
Joshua Frankenfield, Briana M. Sobel, Barbara Chaparro· Proceedings of the Internati...· 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· 0 citations
Two short vignettes and a design guide to help human factors researchers create explanation systems that are practical and trustworthy are presented to show how explainability can become part of the care system instead of being treated as a separate technical feature.
T. Mamun, Laurie Novak, M. Salwei· Proceedings of the Internati...· 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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