The chapter discusses the potential benefits and challenges of generative artificial intelligence (AI) in mental health care. While AI is expected to support routine tasks such as billing, documentation, education, and symptom monitoring, it also has the potential to revolutionize mental health care by improving prevention, diagnosis, and treatment. However, current AI models often focus on perceived empathy rather than clinical outcomes, and there is a lack of evidence on how AI can effectively guide mental health treatment. The authors highlight the need for AI to integrate information from various sources, including clinical exams, patient self-reports, digital phenotyping, genetics, neuroimaging, and clinical judgment, to develop novel diagnostic categories and guide effective treatments. Key challenges include ensuring equity, privacy, and evidence-based practices, as well as addressing the risks of AI bias and the need for robust clinical trials. The integration of AI into the healthcare system requires collaboration with clinicians, patients, family members, administrators, regulators, and AI developers. The authors emphasize the importance of education and training for both patients and clinicians to ensure informed decision-making and avoid potential pitfalls. Additionally, the chapter calls for a public mental health revolution that privileges the voices of those with lived experience of social adversity and mental health issues. It argues that primary prevention strategies should address key social determinants of mental ill-health and involve affected communities in the design, delivery, and evaluation of interventions. This approach is essential to ensure that mental health policies and interventions are relevant, effective, and equitable.The chapter discusses the potential benefits and challenges of generative artificial intelligence (AI) in mental health care. While AI is expected to support routine tasks such as billing, documentation, education, and symptom monitoring, it also has the potential to revolutionize mental health care by improving prevention, diagnosis, and treatment. However, current AI models often focus on perceived empathy rather than clinical outcomes, and there is a lack of evidence on how AI can effectively guide mental health treatment. The authors highlight the need for AI to integrate information from various sources, including clinical exams, patient self-reports, digital phenotyping, genetics, neuroimaging, and clinical judgment, to develop novel diagnostic categories and guide effective treatments. Key challenges include ensuring equity, privacy, and evidence-based practices, as well as addressing the risks of AI bias and the need for robust clinical trials. The integration of AI into the healthcare system requires collaboration with clinicians, patients, family members, administrators, regulators, and AI developers. The authors emphasize the importance of education and training for both patients and clinicians to ensure informed decision-making and avoid potential pitfalls. Additionally, the chapter calls for a public mental health revolution that privileges the voices of those with lived experience of social adversity and mental health issues. It argues that primary prevention strategies should address key social determinants of mental ill-health and involve affected communities in the design, delivery, and evaluation of interventions. This approach is essential to ensure that mental health policies and interventions are relevant, effective, and equitable.