  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.
