Abstract

This research aims to explore the role of Artificial Intelligence (AI) in mental health by addressing the central research question: How does AI play a role in mental health? To answer this, the study conducts a systematic literature review of 80 peer-reviewed articles, examining how AI contributes to key areas of mental health care, including the identification of mental health issues and the provision of support, and its influence on overall mental health outcomes. The review categorizes studies into two core themes: (1) AI for detecting mental health conditions and (2) AI for providing support. Techniques applied across the studies range from machine learning and deep learning to large language models and behavior-sensing technologies. Methodologically, the reviewed literature demonstrates a strong reliance on empirical approaches such as surveys, laboratory experiments, secondary data analysis, and mathematical modeling. In contrast, design science and qualitative methods, which offer real-world insight and user-centered innovation, remain underutilized. The findings underscore the flexibility of AI in mental health applications while highlighting the need for broader methodological diversity. This paper contributes to the growing discourse on AI in mental health and offers guidance for future research, particularly in developing practical, scalable, and user-centered AI tools that enhance access to mental health care and promote early intervention and well-being.

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