Improving Workplace Well-being in Modern Organizations: A Review of Large Language Model-based Mental Health Chatbots

Abstract

The global rise in mental disorders, particularly in workplaces, necessitated innovative and scalable solutions for delivering therapy. Large Language Model (LLM)-based mental health chatbots have rapidly emerged as a promising tool for overcoming the time, cost, and accessibility constraints often associated with traditional mental health therapy. However, LLM-based mental health chatbots are in their nascency, with significant opportunities to enhance their capabilities to operate within organizational contexts. To this end, this research seeks to examine the role and development of LLMs in mental health chatbots over the past half-decade. Through our review, we identified over 50 mental health-related chatbots, including 22 LLM-based models targeting general mental health, depression, anxiety, stress, and suicide ideation. These chatbots are primarily used for emotional support and guidance but often lack capabilities specifically designed for workplace mental health, where such issues are increasingly prevalent. The review covers their development, applications, evaluation, ethical concerns, integration with traditional services, LLM-as-a-service, and various other business implications in organizational settings. We provide a research illustration of how LLM-based approaches could overcome the identified limitations and also offer a system that could help facilitate systematic evaluation of LLM-based mental health chatbots. We offer suggestions for future research tailored to workplace mental health needs.