There have been considerable advancements in artificial intelligence (AI), specifically with generative AI (GAI) models. GAI is a class of algorithms designed to create new data, such as text, images, and audio, that resembles the data on which they have been trained. These models have been recently investigated in medicine, yet the opportunity and utility of GAI in behavioral health are relatively underexplored. In this commentary, we explore the potential uses of GAI in the field of behavioral health, specifically focusing on image generation. We propose the application of GAI for creating personalized and contextually relevant therapeutic interventions and emphasize the need to integrate human feedback into the AI-assisted therapeutics and decision-making process. We report the use of GAI with a case study of behavioral therapy on emotional recognition and management with a three-step process. We illustrate image generation-specific GAI to recognize, express, and manage emotions, featuring personalized content and interactive experiences. Furthermore, we highlighted limitations, challenges, and considerations, including the elements of human emotions, the need for human-AI collaboration, transparency and accountability, potential bias, security, privacy and ethical issues, and operational considerations. Our commentary serves as a guide for practitioners and developers to envision the future of behavioral therapies and consider the benefits and limitations of GAI in improving behavioral health practices and patient outcomes.
