ChatGPT (Generated Pre-Trained Transformer) As an Adjunct to Mental Health Interventions: A Commentary

Objective: Mental health disorders affect one in four people worldwide, and many have limited access to care. Moreover, the World Health Organization’s Special Initiative for Mental Health (2019-2023) aims to explicitly ensure access to quality and affordable care for mental health conditions in low-to-middle income countries. This commentary explores the applications of chatbots based on GPT as an adjunct to mental health interventions. There are various pharmacological and non-pharmacological procedures for treating mental disorders, including psychosocial and behavioral therapies, with Cognitive Behavioral Therapy (CBT) being the best-researched treatment style. Technological advancements are being employed to scale up the delivery of CBT psychosocial and behavioral therapies, with chatbots being a recent technical invention that can provide practical first-level support for mental disorders. Discussion: Artificial Intelligence (AI) has been gaining attention in mental health interventions, and the advancement in Natural Language Processing (NLP) has allowed the development of Generative Pre-trained Transformers (GPT) language models that can understand natural language and produce human-like responses. Chatbots can provide low-cost and easily accessible interventions for people with mental health disorders, and GPT language models can improve the quality of these chatbots by enabling them to understand and generate human-like responses. Conclusion: Mental illnesses are still rising, and many people have limited access to care due to a shortage of mental health professionals, high treatment costs, and other challenges. Therefore, there is a need to explore and implement new interventions, including the use of AI and chatbots, to improve mental health care access and outcomes. Conclusion: The paper discusses the clinical implications of these findings for community service development for this vulnerable group, concluding that RAD was not uncommon among HIV/AIDS-affected children.