%0 Journal Article
%@ 0933-3657
%A Kleine, Anne-Kathrin
%A Kokje, Eesha
%A Hummelsberger, Pia
%A Lermer, Eva
%A Schaffernak, Insa
%A Gaube, Susanne
%D 2025
%F discovery:10204612
%I ELSEVIER
%J Artificial Intelligence in Medicine
%K Artificial intelligence, Mental healthcare, Clinical decision support, Medical device regulation
%T AI-enabled clinical decision support tools for mental healthcare: A product review
%U https://discovery.ucl.ac.uk/id/eprint/10204612/
%V 160
%X The review seeks to promote transparency in the availability of regulated AI-enabled Clinical Decision Support Systems (AI-CDSS) for mental healthcare. From 84 potential products, seven fulfilled the inclusion criteria. The products can be categorized into three major areas: diagnosis of autism spectrum disorder (ASD) based on clinical history, behavioral, and eye-tracking data; diagnosis of multiple disorders based on conversational data; and medication selection based on clinical history and genetic data. We found five scientific articles evaluating the devices' performance and external validity. The average completeness of reporting, indicated by 52 % adherence to the Consolidated Standards of Reporting Trials Artificial Intelligence (CONSORT-AI) checklist, was modest, signaling room for improvement in reporting quality. Our findings stress the importance of obtaining regulatory approval, adhering to scientific standards, and staying up-to-date with the latest changes in the regulatory landscape. Refining regulatory guidelines and implementing effective tracking systems for AI-CDSS could enhance transparency and oversight in the field.
%Z © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).