Evaluation Practices and Characteristics of Implementation for Clinical Artificial Intelligence in Mental Healthcare: A Rapid Scoping Review
Abstract
Artificial intelligence (AI) is increasingly used in mental healthcare to support diagnosis, risk assessment, treatment, and service delivery. Despite rapid innovation, the translation of these tools into routine clinical practice remains limited. A major barrier is the lack of consensus on how to evaluate whether a clinical AI tool has sufficient evidence to be safely, effectively, and equitably implemented. Existing evaluation approaches vary widely, with some prioritizing technical performance while others emphasize fairness, usability, governance, or workflow integration. This fragmentation creates uncertainty for clinicians, health systems, and policymakers. This lack of standardization is particularly concerning when selecting and implementing AI tools in mental health settings where data is highly sensitive and vulnerable groups are at a heightened risk of harm caused by insufficient evaluation. This paper presents a rapid scoping review that details the implementation characteristics, evaluation frameworks, methods, and criteria used to assess clinical AI tools for readiness in mental healthcare. Utilizing the PRISMA-ScR checklist, searches were conducted across multiple academic databases and 40 studies met the inclusion criteria. Data were charted across domains including technical performance, clinical validity, fairness and equity, operational feasibility, governance, and explicit or implicit thresholds for implementation readiness. Findings were summarized using descriptive analyses and narrative synthesis to identify patterns and gaps across implementation and evaluation approaches. The results provide an evidence base intended to help inform the development of future evaluation guidance to support safer decision-making about the clinical deployment of AI in mental healthcare.
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BibTeX
@misc{kemp2026evaluation,
title = {{Evaluation Practices and Characteristics of Implementation for Clinical Artificial Intelligence in Mental Healthcare: A Rapid Scoping Review}},
author = {Jessica Kemp and Charlotte Pape and Masooma Hassan and Ian Edwards and Nuzhat Jahin and Benjamin Rosen and Emily Bowman and Sanjeev Sockalingam and Aristotle Voineskos and Andrea Cipriani},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/xbu63_v3},
url = {https://osf.io/preprints/psyarxiv/xbu63_v3/},
note = {CC0},
}
RIS
TY - UNPB
TI - Evaluation Practices and Characteristics of Implementation for Clinical Artificial Intelligence in Mental Healthcare: A Rapid Scoping Review
AU - Jessica Kemp
AU - Charlotte Pape
AU - Masooma Hassan
AU - Ian Edwards
AU - Nuzhat Jahin
AU - Benjamin Rosen
AU - Emily Bowman
AU - Sanjeev Sockalingam
AU - Aristotle Voineskos
AU - Andrea Cipriani
PY - 2026
DA - 2026/09/08
PB - PsyArXiv
DO - 10.31234/osf.io/xbu63_v3
UR - https://osf.io/preprints/psyarxiv/xbu63_v3/
AB - Artificial intelligence (AI) is increasingly used in mental healthcare to support diagnosis, risk assessment, treatment, and service delivery. Despite rapid innovation, the translation of these tools into routine clinical practice remains limited. A major barrier is the lack of consensus on how to evaluate whether a clinical AI tool has sufficient evidence to be safely, effectively, and equitably implemented. Existing evaluation approaches vary widely, with some prioritizing technical performance while others emphasize fairness, usability, governance, or workflow integration. This fragmentation creates uncertainty for clinicians, health systems, and policymakers. This lack of standardization is particularly concerning when selecting and implementing AI tools in mental health settings where data is highly sensitive and vulnerable groups are at a heightened risk of harm caused by insufficient evaluation. This paper presents a rapid scoping review that details the implementation characteristics, evaluation frameworks, methods, and criteria used to assess clinical AI tools for readiness in mental healthcare. Utilizing the PRISMA-ScR checklist, searches were conducted across multiple academic databases and 40 studies met the inclusion criteria. Data were charted across domains including technical performance, clinical validity, fairness and equity, operational feasibility, governance, and explicit or implicit thresholds for implementation readiness. Findings were summarized using descriptive analyses and narrative synthesis to identify patterns and gaps across implementation and evaluation approaches. The results provide an evidence base intended to help inform the development of future evaluation guidance to support safer decision-making about the clinical deployment of AI in mental healthcare.
ER -