@article{discovery10204612,
            year = {2025},
         journal = {Artificial Intelligence in Medicine},
       publisher = {ELSEVIER},
           title = {AI-enabled clinical decision support tools for mental healthcare: A product review},
          volume = {160},
            note = {{\copyright} 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/).},
           month = {February},
          author = {Kleine, Anne-Kathrin and Kokje, Eesha and Hummelsberger, Pia and Lermer, Eva and Schaffernak, Insa and Gaube, Susanne},
        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.},
        keywords = {Artificial intelligence, Mental healthcare, Clinical decision support, Medical device regulation},
            issn = {0933-3657},
             url = {https://doi.org/10.1016/j.artmed.2024.103052}
}