An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives (2023)

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.

## introduction the proliferation of conversational agents (cas), also known as chatbots or dialog systems, has been spurred by advancements in natural language processing (nlp) technologies. their application spans diverse sectors, from education @xcite ) to e-commerce @xcite , demonstrating their increasing ubiquity and potency. the utility of cas within the mental health domain has been gaining recognition. over 30% of the world's population suffers from one or more mental health conditions; about 75% individuals in low and middle-income countries and about 50% individuals in high-income countries do not receive care and treatment @xcite . the sensitive (and often stigmatized) nature of mental health discussions further exacerbates this problem, as many individuals find it difficult to disclose their struggles openly @xcite . conversational agents like woebot @xcite and wysa @xcite were some of the first mobile applications to address this issue. they provide an accessible and considerably less intimidating platform for mental health support, thereby assisting a substantial number of individuals. their effectiveness highlights the potential of mental health-focused cas as one of the viable solutions to ease the mental health disclosure and treatment gap. despite the successful implementation of certain cas in mental health, a significant disconnect persists between research in computer science (cs) and medicine. this disconnect is particularly evident when we consider the limited adoption of advanced nlp (e.g. large language models) models in the research published in medicine. while cs researchers have made substantial strides in nlp, there is a lack of focus on the human evaluation and direct impacts these developments have on patients. furthermore, we observe that mental health cas are drawing significant attention in medicine, yet remain underrepresented in health-applicationsfocused research in nlp. this imbalance calls for a more integrated approach in future studies to optimize the potential of these evolving technologies for mental health applications. in this paper, we present a comprehensive analysis of academic research related to mental health conversational agents, conducted within the domains of cs and medicine 1 . employing the preferred reporting items for systematic reviews and meta-analyses (prisma) framework @xcite , we systematically reviewed 136 pertinent papers to discern the trends and research directions in the domain of mental health conversational agents over the past five years. we find that there is a disparity in research focus and technology across communities, which is also shown in the differences in evaluation. furthermore, we point out the issues that apply across domains, including transparency and language/cultural heterogeneity. the primary objective of our study is to conduct a systematic and transparent review of mental health ca research papers across the domains of cs and medicine. this process aims not only to bridge the existing gap between these two broad disciplines but also to facilitate reciprocal learning and strengths sharing. in this paper, we aim to address the following key questions: ## prior survey papers mental health conversational agents are discussed in several non-cs survey papers, with an emphasis on their usability in psychiatry @xcite @xcite , and users' acceptability @xcite . these survey papers focus on underpinning theory @xcite , standardized psychological outcomes for evaluation @xcite in addition to accessibility @xcite , safety @xcite and validity @xcite of cas. contrary to surveys for medical audiences, nlp studies mostly focus on the quality of the generated response from the standpoint of text generation. @xcite in their latest survey, reviewed 70 articles and investigated task-oriented healthcare dialogue systems from a technical perspective. the discussion focuses on the system architecture and design of cas. the majority of healthcare cas were found to have pipeline architecture despite the growing popularity of end-toend architectures in the nlp domain. a similar technical review by safi et al. (2020) also reports a high reliance on static dialogue systems in cas developed for medical applications. task-oriented dialogue systems usually deploy a guided conversation style which fits well with rule-based systems. however, su et al. (2020); abd-alrazaq et al. (2021) pointed to the problem of robotic conversation style in mental health apps where users prefer an unconstrained conversation style and may even want to lead the conversation (abd-alrazaq et al., 2019). huang (2022) further underlines the need for self-evolving cas to keep up with evolving habits and topics during the course of app usage. surveys from the rest of cs cover hci (de souza et al., 2022) and the system design of cas (dev et al., 2022; narynov et al., 2021a). de souza et al. (2022) analyzed 6 mental health mobile applications from an hci perspective and suggested 24 design considerations including empathetic conversation style, probing, and session duration for effective dialogue. damij and bhattacharya (2022) proposed three key dimensions namely people (citizen centric goals ), process (regulations and governance) and ai technology to consider when designing public care cas. these survey papers independently provide an in-depth understanding of advancements and challenges in the cs and medical domains. however, there is a lack of studies that can provide a joint appraisal of developments to enable cross-learning across these domains. with this goal, we consider research papers from medicine (pubmed), nlp (the acl anthology), and the rest of cs (acm, aaai, ieee) to examine the disparities in goals, methods, and evaluations of research related to mental health conversational agents. ## conclusion in this paper, we used the prisma framework to systematically review the recent studies about mental health ca across both cs and medical domains. from the well-represented databases in both domains, we begin with 865 papers based on a keyword search to identify mental health-related conversational agent papers and use title, abstract, and full-text screening to retain 136 papers that fit our inclusion criteria. furthermore, we extract a wide range of features from model and experiment papers, summarizing attributes in the fields of general features, techniques, appearance, and experiment. based on this information, we find that there is a gap between cs and medicine in mental health ca studies. they vary in research focus, technology, and evaluation purposes. we also identify common issues that lie between domains, including transparency and language/cultural heterogeneity.