Readiness Evaluation for AI-Mental Health Deployment and Implementation (READI): A review and proposed framework

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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