Towards Explainable and Safe Conversational Agents for Mental Health: A Survey
It tackles the problem of building responsible and trustworthy VMHAs for mental health support, which is incremental as it builds on existing systems by adding explainability and safety features.
This survey addresses the need for more comprehensive, safe, and explainable Virtual Mental Health Assistants (VMHAs) to improve patient interactions, by reviewing existing conversational agents and proposing new directions for enhancements in contextual knowledge, datasets, and clinical decision support.
Virtual Mental Health Assistants (VMHAs) are seeing continual advancements to support the overburdened global healthcare system that gets 60 million primary care visits, and 6 million Emergency Room (ER) visits annually. These systems are built by clinical psychologists, psychiatrists, and Artificial Intelligence (AI) researchers for Cognitive Behavioral Therapy (CBT). At present, the role of VMHAs is to provide emotional support through information, focusing less on developing a reflective conversation with the patient. A more comprehensive, safe and explainable approach is required to build responsible VMHAs to ask follow-up questions or provide a well-informed response. This survey offers a systematic critical review of the existing conversational agents in mental health, followed by new insights into the improvements of VMHAs with contextual knowledge, datasets, and their emerging role in clinical decision support. We also provide new directions toward enriching the user experience of VMHAs with explainability, safety, and wholesome trustworthiness. Finally, we provide evaluation metrics and practical considerations for VMHAs beyond the current literature to build trust between VMHAs and patients in active communications.