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Kleine, Anne-Kathrin ORCID logoORCID: https://orcid.org/0000-0003-1919-2834; Kokje, Eesha ORCID logoORCID: https://orcid.org/0000-0001-9341-2247; Hummelsberger, Pia; Lermer, Eva ORCID logoORCID: https://orcid.org/0000-0002-6600-9580; Schaffernak, Insa ORCID logoORCID: https://orcid.org/0009-0004-2024-099X und Gaube, Susanne ORCID logoORCID: https://orcid.org/0000-0002-1633-4772 (February 2025): AI-enabled clinical decision support tools for mental healthcare: A product review. In: Artificial Intelligence in Medicine, Vol. 160, 103052 [PDF, 1MB]

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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.

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