An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives
This survey bridges the gap between computer science and medicine for mental health conversational agents, offering recommendations to foster cross-disciplinary development, though it is incremental as it synthesizes existing knowledge.
The authors conducted a systematic review of 534 papers from computer science and medicine to address the disciplinary divide in mental health conversational agents, revealing 136 key papers with distinct focuses: computer science emphasizes LLM techniques and automated metrics, while medicine uses rule-based agents and health outcome metrics.
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.