AI has been proven to be a valuable tool for diagnosing various mental health conditions. It can analyze data from multiple sources, including medical records, internet searches, wearable devices, and social media networks. It can integrate different datasets and aid in mental illness diagnosis [2].

Additionally, AI can assist with administrative tasks such as scheduling appointments, managing patient records, facilitating routine communication, answering frequently asked questions, and verifying insurance benefits [1,3]. By automating these tasks, AI can reduce the burden on healthcare professionals, allowing them to focus more on direct patient care.

AI can aid early detection and intervention for individuals at risk of developing mental health issues. It can also be integrated into various clinical tools, such as digital therapeutics, which provide evidence-based programs to reduce anxiety and depression. AI-powered interventions, including cognitive behavioral therapy (CBT)-based tools, can help individuals manage depressive symptoms and insomnia [1,3].

Natural language processing algorithms can analyze written and spoken language-including chats, emails, and social media posts-to detect patterns associated with mental health conditions. The widespread use of smartphones makes natural language processing a cost-effective method for tracking mental health, as these devices can store a significant amount of personal data to reveal linguistic patterns linked to conditions such as depression [4].

Several studies have suggested that chatbots can detect mental health issues by interacting with individuals similarly to mental health professionals. Chatbots can assess mood, stress levels, energy levels, and sleep patterns [5,6]. They can also analyze patient responses, suggest therapeutic interventions including behavioral changes, exercise, meditation, and relaxation techniques, and recommend seeking medical treatments such as pharmaceutical therapy [4].

Furthermore, AI-enabled wearable devices can monitor symptoms and provide real-time feedback to both patients and healthcare providers. These devices can perform continuous monitoring of mental health conditions, support timely application of therapeutic interventions, and help assess treatment outcomes [1].

AI can contribute to the development of training programs for helping health care professionals recognize and manage stress effectively [7]. Many graduate students are beginning to learn how to provide therapy by engaging with AI-driven case studies, such as virtual reality (VR) and simulation-based training.

The use of AI in mental health care raises significant ethical concerns. Conversational artificial intelligence (CAI) apps such as psychotherapeutic chatbots present several ethical challenges. Studies have reported 'safety and harm' and 'the risk of dependency’ of using CAI systems [8].

Additionally, AI-driven mental health care support comes with risks [9]. For people using AI for mental health support, there is a risk of misdiagnosis or misinformation due to potential errors of AI algorithms. There are also concerns about the lack of empathy of AI systems, which may impact the quality of care. Furthermore, data privacy is a critical ethical issue as AI systems process sensitive personal information.

While AI offers advantages in mental health care such as improved access to care, cost-effectiveness, personalization, and work efficiency, several concerns must be addressed, including reductions of human connection, ethical and privacy risks, regulatory challenges, medical error, misuse, and data security. Therefore, integrating AI into mental health care systems must be approached with caution, ensuring that legal and ethical issues are addressed and safeguards are in place to minimize potential harm.