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Conversational agents for depression screening: A systematic review.

Iván Otero-González1, Moisés R Pacheco-Lorenzo1, Manuel J Fernández-Iglesias1

  • 1atlanTTic, University of Vigo, Vigo, Spain.

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This summary is machine-generated.

This study reviewed 30 conversational agents for depression screening, finding voice interaction enhances their effectiveness. These AI tools show promise for mental health monitoring in patients and caregivers.

Keywords:
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Area of Science:

  • * Mental Health Technology
  • * Artificial Intelligence in Healthcare
  • * Human-Computer Interaction

Background:

  • * Conversational agents are increasingly explored for mental health disorder detection, particularly depression screening.
  • * Existing research focuses on voice-based interactions, but text-based and avatar approaches are also considered.

Approach:

  • * A systematic literature review using PRISMA methodology was conducted across major scientific databases.
  • * Studies were selected based on effectiveness, usability, personalization, and psychometric properties for depression detection.
  • * Analysis included 36 selected studies from an initial retrieval of 993 references.

Key Points:

  • * Thirty conversational agents were identified for depression detection, often integrated with anxiety or stress disorder screening.
  • * Most agents utilize standardized clinical tests (e.g., Patient Health Questionnaire, Beck Depression Inventory) for screening and validation.
  • * Target user profiles include patients (33%) and caregivers (11%), with many agents lacking a clearly defined profile.

Conclusions:

  • * Conversational agents offer a valuable tool for mental health screening across diverse user groups.
  • * While text-based interaction is common, voice integration is a growing trend, enhancing naturalness and perceived invasiveness.
  • * Combined approaches in conversational agents can enrich data acquisition for more comprehensive mental health assessments.