TY  - JOUR
UR  - https://doi.org/10.1016/j.artmed.2024.103052
PB  - ELSEVIER
SN  - 0933-3657
N2  - 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.
ID  - discovery10204612
A1  - Kleine, Anne-Kathrin
A1  - Kokje, Eesha
A1  - Hummelsberger, Pia
A1  - Lermer, Eva
A1  - Schaffernak, Insa
A1  - Gaube, Susanne
KW  - Artificial intelligence
KW  -  Mental healthcare
KW  -  Clinical decision support
KW  -  Medical device regulation
JF  - Artificial Intelligence in Medicine
AV  - public
Y1  - 2025/02//
VL  - 160
EP  - 11
TI  - AI-enabled clinical decision support tools for mental healthcare: A product review
N1  - © 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/).
ER  -