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    Area of Science:

    • Mental Health Technology
    • Clinical Informatics
    • Artificial Intelligence in Healthcare

    Background:

    • Depression is a complex, multifactorial condition requiring personalized treatment approaches.
    • Existing treatment strategies for depression may not adequately address diverse patient populations (e.g., postpartum, adolescent, elderly).
    • The integration of Artificial Intelligence (AI) offers potential for enhancing clinical decision-making in mental health.

    Purpose of the Study:

    • To systematically review AI-based Clinical Decision Support Systems (CDSS) for depression treatment.
    • To evaluate the feasibility, acceptability, and potential of AI-CDSS in personalized depression care.
    • To identify implementation strategies and challenges for AI-CDSS in clinical settings.

    Main Methods:

    • Systematic review of studies on AI-based CDSS for depression.
    • Analysis of algorithms, performance metrics, and system implementations.
    • Categorization of systems by purpose: treatment selection, prediction/risk assessment, clinical support/data review.

    Main Results:

    • Identified 11 unique algorithms, 9 performance metrics, and 10 CDSS implementations.
    • AI-CDSS demonstrated positive results in supporting clinical decisions.
    • Key challenges include small/non-diverse samples, immature methodologies, workflow integration issues, data privacy, and ethical concerns.

    Conclusions:

    • AI-based CDSS hold significant potential for improving depression patient care.
    • Further research requires addressing foundational issues like sample diversity and robust model development.
    • Future advancements may include VR integration and multi-condition prediction for AI in mental health.