Large language models for mental health diagnosis and treatment: a survey

Mental health (MeHE) is a fundamental dimension of human well-being that encompasses emotional, psychological, and social aspects. Effective MeHE management enables individuals to cope with stress, maintain healthy relationships, and achieve their personal and social goals. However, traditional approaches are often inadequate in addressing the multidimensional challenges of early detection, personalized interventions, and comprehensive MeHE education. Large Language Models offer a transformative approach to the field of MeHE. With the ability to process large and complex textual data, these models can identify behavioral patterns in patients’ responses, suggest personalized interventions, and improve access to MeHE resources. Despite these advances, significant challenges remain. Applying reinforcement learning techniques to MeHE applications necessitates addressing challenges such as model-driven bias, protecting sensitive information, and providing robust evidence of clinical performance. This review systematically examines the applications of large language models in MeHE, providing a comprehensive analysis of their capabilities and limitations. This study examined how large language models address existing challenges, including early diagnosis, personalized treatments, and effective public education. Findings show that large language models increased the accuracy of early diagnosis of mental disorders by 33%, the effectiveness of personalized treatment plans by 27%, and participation in MeHE education and awareness by 24%. Ultimately, this research underscores the pivotal role of large language models in promoting MeHE. By providing practical insights and suggesting strategies to overcome implementation challenges, this review lays the groundwork for developing innovative, effective, and equitable solutions in the field of MeHE.