{"title":"焦点,干预特异性和滴定:哪些人工智能工具适合精神卫生保健?","authors":"Joshua S Steinberg, William Thies, John R Weisz","doi":"10.1037/ccp0001029","DOIUrl":null,"url":null,"abstract":"<p><p>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).</p>","PeriodicalId":15447,"journal":{"name":"Journal of consulting and clinical psychology","volume":"94 8","pages":"453-455"},"PeriodicalIF":6.0000,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Focal, intervention-specific, and titrated: Which artificial intelligence tools are FIT for mental health care?\",\"authors\":\"Joshua S Steinberg, William Thies, John R Weisz\",\"doi\":\"10.1037/ccp0001029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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).</p>\",\"PeriodicalId\":15447,\"journal\":{\"name\":\"Journal of consulting and clinical psychology\",\"volume\":\"94 8\",\"pages\":\"453-455\"},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2026-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of consulting and clinical psychology\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1037/ccp0001029\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, CLINICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of consulting and clinical psychology","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/ccp0001029","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, CLINICAL","Score":null,"Total":0}
Focal, intervention-specific, and titrated: Which artificial intelligence tools are FIT for mental health care?
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).
期刊介绍:
The Journal of Consulting and Clinical Psychology® (JCCP) publishes original contributions on the following topics: the development, validity, and use of techniques of diagnosis and treatment of disordered behaviorstudies of a variety of populations that have clinical interest, including but not limited to medical patients, ethnic minorities, persons with serious mental illness, and community samplesstudies that have a cross-cultural or demographic focus and are of interest for treating behavior disordersstudies of personality and of its assessment and development where these have a clear bearing on problems of clinical dysfunction and treatmentstudies of gender, ethnicity, or sexual orientation that have a clear bearing on diagnosis, assessment, and treatmentstudies of psychosocial aspects of health behaviors. Studies that focus on populations that fall anywhere within the lifespan are considered. JCCP welcomes submissions on treatment and prevention in all areas of clinical and clinical–health psychology and especially on topics that appeal to a broad clinical–scientist and practitioner audience. JCCP encourages the submission of theory–based interventions, studies that investigate mechanisms of change, and studies of the effectiveness of treatments in real-world settings. JCCP recommends that authors of clinical trials pre-register their studies with an appropriate clinical trial registry (e.g., ClinicalTrials.gov, ClinicalTrialsRegister.eu) though both registered and unregistered trials will continue to be considered at this time.