AI-enabled clinical decision support tools for mental healthcare: A product review.

Journal: Artificial intelligence in medicine
Published Date:

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.

Authors

  • Anne-Kathrin Kleine
    Center for Leadership and People Management, Department of Psychology, LMU Munich, Geschwister-Scholl-Platz 1, 80539, Munich, Germany.
  • Eesha Kokje
    LMU Munich, Germany.
  • Pia Hummelsberger
    LMU Center for Leadership and People Management, Department of Psychology, LMU Munich, Munich, Germany.
  • Eva Lermer
    LMU Center for Leadership and People Management, Department of Psychology, LMU Munich, Munich, Germany.
  • Insa Schaffernak
    Technical University of Applied Sciences Augsburg, Germany.
  • Susanne Gaube
    UCL Global Business School for Health, University College London, London, United Kingdom.