ArticleTechnology, mind, and behavior2025
Readiness Evaluation for AI-Mental Health Deployment and Implementation (READI): A Review and Proposed Framework.
Article in Technology, mind, and behavior, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Frameworks, Methodologies, and Tools for Evaluating Large Language Models in Digital Mental Health Interventions: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- Exploring Real-World Use of AI Chatbots for Mental Health Support: Cross-Sectional Survey Study.JMIR mental health · 2026Article
- A scoping review on the mental health harms of LLM-based chatbots.NPJ digital medicine · 2026Article
- Exploring Applications of AI in the Crisis Line Sector: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- Real-world use of large language models for mental health in 2024.NPJ digital medicine · 2026Article
- An Acceptance Criteria Framework for Determining the Implementation Fit of Custom Large Language Models in Public Health Interventions.Journal of medical Internet research · 2026Article
- Artificial intelligence in traumatic stress treatment: The TRUST framework for ethical development, clinical applications, and research advancement.Journal of traumatic stress · 2026Review
- Exploring Students' Perceptions and Usage of Artificial Intelligence in Supporting Mental Health: A Preliminary Study in Higher Education in Qatar.Healthcare (Basel, Switzerland) · 2026Article
- Cybersecurity and Privacy Risks of Generative AI Mental-Health Chatbots: A Systematic Review and Regulatory Framework.Journal of multidisciplinary healthcare · 2026Review
- Accelerating Digital Mental Health: The Society of Digital Psychiatry's Three-Pronged Road Map for Education, Digital Navigators, and AI.JMIR mental health · 2025Article
- A Prompt Engineering Framework for Large Language Model-Based Mental Health Chatbots: Conceptual Framework.JMIR mental health · 2025Article
- Generative artificial intelligence in posttraumatic stress disorder treatment: Exploring five different use cases.Journal of traumatic stress · 2025Article
- A Comparison of Responses from Human Therapists and Large Language Model-Based Chatbots to Assess Therapeutic Communication: Mixed Methods Study.JMIR mental health · 2025Article
- Artificial Intelligence in Obsessive-Compulsive Disorder: A Systematic Review.Current treatment options in psychiatry · 2025Review
- Generating Job Recommendations for People With Schizophrenia Spectrum Disorder Using Gemini 2.0 Flash and Claude Sonnet 4: An Exploratory Analysis.Healthcare technology lettersArticle
- Generative AI versus human conversational agent for reducing procrastination: A randomized pilot trial.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
While generative artificial intelligence (AI) may lead to technological advances in the mental health field, it poses safety risks for mental health service consumers. Furthermore, clinicians and healthcare systems must attend to safety and ethical considerations prior to deploying these AI-mental health technologies. To ensure the responsible deployment of AI-mental health applications, a principled method for evaluating and reporting on AI-mental health applications is needed. We conducted a narrative review of existing frameworks and criteria (from the mental health, healthcare, and AI fields) relevant to the evaluation of AI-mental health applications. We provide a summary and analysis of these frameworks, with a particular emphasis on the unique needs of the AI-mental health intersection. Existing frameworks contain areas of convergence (e.g., frequent emphasis on safety, privacy/confidentiality, effectiveness, and equity) that are relevant to the evaluation of AI-mental health applications. However, current frameworks are insufficiently tailored to unique considerations for AI and mental health. To address this need, we introduce the Readiness Evaluation for AI-Mental Health Deployment and Implementation (READI) framework for mental health applications. The READI framework comprises considerations of Safety, Privacy/Confidentiality, Equity, Effectiveness, Engagement, and Implementation. The READI framework outlines key criteria for assessing the readiness of AI-mental health applications for clinical deployment, offering a structured approach for evaluating these technologies and reporting findings.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.