Large language models (LLMs) offer significant potential to address the growing global demand for mental health support, but also pose risks that must be carefully managed. This paper summarizes existing research on the application of LLMs in mental health education, assessment, and intervention, highlighting opportunities and risks. LLMs can provide mental health education by generating accurate and helpful information, support provider training through efficient content creation, and assist in mental health assessments by identifying symptoms and diagnoses. However, LLMs may produce inaccurate or biased information, perpetuate inequalities, and fail to provide ethical or reliable mental health services. LLMs can also be used in mental health interventions, such as chatbots, which can offer support for depression and anxiety. However, these chatbots may not always provide appropriate or safe advice, and may fail to address suicide risk effectively. The use of LLMs in mental health must be ethically responsible, with careful consideration of data quality, model reliability, and transparency. LLMs should be fine-tuned for mental health, ensure equity, and adhere to ethical standards. Human involvement is critical throughout the development, testing, and deployment of mental health LLMs to ensure they are safe, effective, and equitable. The paper emphasizes the need for responsible development, testing, and deployment of mental health LLMs, with a focus on ensuring that they are used to enhance, rather than replace, human mental health care. It calls for ongoing research and collaboration to improve the accuracy, reliability, and ethical use of LLMs in mental health. The potential of LLMs to improve mental health care is significant, but must be balanced with the need to protect individuals and ensure that mental health services are accessible and equitable for all.Large language models (LLMs) offer significant potential to address the growing global demand for mental health support, but also pose risks that must be carefully managed. This paper summarizes existing research on the application of LLMs in mental health education, assessment, and intervention, highlighting opportunities and risks. LLMs can provide mental health education by generating accurate and helpful information, support provider training through efficient content creation, and assist in mental health assessments by identifying symptoms and diagnoses. However, LLMs may produce inaccurate or biased information, perpetuate inequalities, and fail to provide ethical or reliable mental health services. LLMs can also be used in mental health interventions, such as chatbots, which can offer support for depression and anxiety. However, these chatbots may not always provide appropriate or safe advice, and may fail to address suicide risk effectively. The use of LLMs in mental health must be ethically responsible, with careful consideration of data quality, model reliability, and transparency. LLMs should be fine-tuned for mental health, ensure equity, and adhere to ethical standards. Human involvement is critical throughout the development, testing, and deployment of mental health LLMs to ensure they are safe, effective, and equitable. The paper emphasizes the need for responsible development, testing, and deployment of mental health LLMs, with a focus on ensuring that they are used to enhance, rather than replace, human mental health care. It calls for ongoing research and collaboration to improve the accuracy, reliability, and ethical use of LLMs in mental health. The potential of LLMs to improve mental health care is significant, but must be balanced with the need to protect individuals and ensure that mental health services are accessible and equitable for all.