An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives

TL;DR

This comprehensive review analyzes 534 papers on mental health chatbots from both computer science and medical perspectives, highlighting differences and proposing recommendations to foster interdisciplinary development.

Contribution

It bridges the gap between CS and medical research on mental health chatbots through a systematic review and cross-disciplinary insights.

Findings

CS focuses on LLM techniques and automated response metrics.

Medical research emphasizes rule-based agents and health outcome metrics.

Recommendations for transparency, ethics, and cultural considerations.

Abstract

Mental health conversational agents (a.k.a. chatbots) are widely studied for their potential to offer accessible support to those experiencing mental health challenges. Previous surveys on the topic primarily consider papers published in either computer science or medicine, leading to a divide in understanding and hindering the sharing of beneficial knowledge between both domains. To bridge this gap, we conduct a comprehensive literature review using the PRISMA framework, reviewing 534 papers published in both computer science and medicine. Our systematic review reveals 136 key papers on building mental health-related conversational agents with diverse characteristics of modeling and experimental design techniques. We find that computer science papers focus on LLM techniques and evaluating response quality using automated metrics with little attention to the application while medical papers use rule-based conversational agents and outcome metrics to measure the health outcomes of participants. Based on our findings on transparency, ethics, and cultural heterogeneity in this review, we provide a few recommendations to help bridge the disciplinary divide and enable the cross-disciplinary development of mental health conversational agents.