Generative artificial intelligence (AI) is increasingly being integrated into mental health care, with potential benefits in routine tasks such as billing, documentation, education, and symptom monitoring. However, the impact on mental health care's nature, particularly in prevention, diagnosis, and treatment, remains underexplored. While AI may enhance text-based support programs, current assessments focus on perceived empathy rather than clinical outcomes. AI's role in diagnosis is limited, as existing models struggle to replicate clinical reality. Future AI diagnosis could be more inclusive, drawing from diverse data sources and being outcomes-driven. AI's potential in treatment is promising, but current studies lack robust evidence, and there are concerns about bias and the accuracy of AI-generated recommendations. AI's integration into mental health care requires addressing equity, privacy, evidence, clinical engagement, and interoperability. Current datasets are often non-psychiatric, and AI chatbots are not intended for clinical use. Privacy concerns are significant, as AI models may process sensitive health information without proper consent. The European Union and the US are exploring regulations to ensure compliance with data protection laws. The potential of AI in mental health care is vast, but it requires robust evidence, ethical considerations, and collaboration with patients, clinicians, and regulators. AI must be well-integrated into the healthcare system, with a focus on engagement, education, and the development of personalized, culturally responsive support. The public mental health revolution must prioritize lived experience voices and create alliances with affected communities to ensure that interventions are effective, equitable, and inclusive.Generative artificial intelligence (AI) is increasingly being integrated into mental health care, with potential benefits in routine tasks such as billing, documentation, education, and symptom monitoring. However, the impact on mental health care's nature, particularly in prevention, diagnosis, and treatment, remains underexplored. While AI may enhance text-based support programs, current assessments focus on perceived empathy rather than clinical outcomes. AI's role in diagnosis is limited, as existing models struggle to replicate clinical reality. Future AI diagnosis could be more inclusive, drawing from diverse data sources and being outcomes-driven. AI's potential in treatment is promising, but current studies lack robust evidence, and there are concerns about bias and the accuracy of AI-generated recommendations. AI's integration into mental health care requires addressing equity, privacy, evidence, clinical engagement, and interoperability. Current datasets are often non-psychiatric, and AI chatbots are not intended for clinical use. Privacy concerns are significant, as AI models may process sensitive health information without proper consent. The European Union and the US are exploring regulations to ensure compliance with data protection laws. The potential of AI in mental health care is vast, but it requires robust evidence, ethical considerations, and collaboration with patients, clinicians, and regulators. AI must be well-integrated into the healthcare system, with a focus on engagement, education, and the development of personalized, culturally responsive support. The public mental health revolution must prioritize lived experience voices and create alliances with affected communities to ensure that interventions are effective, equitable, and inclusive.