Toward a Framework for Assessing Patient-Facing Gen AI Tools in Mental Healthcare.
Generative artificial intelligence (Gen AI) is becoming increasingly used in mental healthcare. While these systems offer scalable, human-like interactions, concerns persist regarding safety, clinical validity, and the absence of systematic assessment frameworks.
This study systematically reviews existing research on Gen-AI mental-health applications to synthesize current evaluation practices and identify key methodological steps needed for pre-deployment assessment.
A systematic search was conducted across major databases (PubMed, IEEE Xplore, and PsycINFO) following PRISMA guidelines. Studies published between 2017 and 2024 examining Gen-AI tools for mental healthcare were included. The final sample of 72 studies was explored to identify assessment domains.
Four domains of assessment emerged: technical performance, which focuses on model accuracy and reliability on mental-health tasks; clinical validity, which examines alignment with evidence-based guidelines and clinician judgments; expert perceptions, which assess perceived quality and safety of AI-generated outputs; and user perceptions, which capture how end-users experience, interpret, and trust these responses.
Findings highlight the need for unified, multilayered frameworks to guide the safe and responsible deployment of patient-facing Gen-AI mental-health tools. The review proposes a high-level pre-deployment assessment framework to support researchers, developers, clinicians, and regulators.
This study systematically reviews existing research on Gen-AI mental-health applications to synthesize current evaluation practices and identify key methodological steps needed for pre-deployment assessment.
A systematic search was conducted across major databases (PubMed, IEEE Xplore, and PsycINFO) following PRISMA guidelines. Studies published between 2017 and 2024 examining Gen-AI tools for mental healthcare were included. The final sample of 72 studies was explored to identify assessment domains.
Four domains of assessment emerged: technical performance, which focuses on model accuracy and reliability on mental-health tasks; clinical validity, which examines alignment with evidence-based guidelines and clinician judgments; expert perceptions, which assess perceived quality and safety of AI-generated outputs; and user perceptions, which capture how end-users experience, interpret, and trust these responses.
Findings highlight the need for unified, multilayered frameworks to guide the safe and responsible deployment of patient-facing Gen-AI mental-health tools. The review proposes a high-level pre-deployment assessment framework to support researchers, developers, clinicians, and regulators.