eprintid: 10204612
rev_number: 6
eprint_status: archive
userid: 699
dir: disk0/10/20/46/12
datestamp: 2025-02-12 08:25:20
lastmod: 2025-02-12 08:25:20
status_changed: 2025-02-12 08:25:20
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Kleine, Anne-Kathrin
creators_name: Kokje, Eesha
creators_name: Hummelsberger, Pia
creators_name: Lermer, Eva
creators_name: Schaffernak, Insa
creators_name: Gaube, Susanne
title: AI-enabled clinical decision support tools for mental healthcare: A product review
ispublished: pub
divisions: UCL
divisions: B02
keywords: Artificial intelligence, Mental healthcare, Clinical decision support, Medical device regulation
note: © 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/).
abstract: 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.
date: 2025-02
date_type: published
publisher: ELSEVIER
official_url: https://doi.org/10.1016/j.artmed.2024.103052
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2343701
doi: 10.1016/j.artmed.2024.103052
medium: Print-Electronic
pii: S0933-3657(24)00294-X
lyricists_name: Gaube, Susanne
lyricists_id: SGAUB48
actors_name: Gaube, Susanne
actors_id: SGAUB48
actors_role: owner
funding_acknowledgements: 98525 [VW Foundation ("Human-Al-Inter-action in Healthcare: Identifying Factors Contributing to Clinical Utility")]
full_text_status: public
publication: Artificial Intelligence in Medicine
volume: 160
article_number: 103052
pages: 11
event_location: Netherlands
issn: 0933-3657
citation:        Kleine, Anne-Kathrin;    Kokje, Eesha;    Hummelsberger, Pia;    Lermer, Eva;    Schaffernak, Insa;    Gaube, Susanne;      (2025)    AI-enabled clinical decision support tools for mental healthcare: A product review.                   Artificial Intelligence in Medicine , 160     , Article 103052.  10.1016/j.artmed.2024.103052 <https://doi.org/10.1016/j.artmed.2024.103052>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10204612/1/%28Kleine%20et%20al.%2C%202025%29.pdf