Artificial intelligence offers transformative potential to improve mental health research and care, particularly through clinical decision support systems that are poised for widespread adoption in routine practice[2]. Machine learning algorithms are used to analyze complex biological data, including imaging techniques, genetics, and molecular signals, increasing the accuracy of diagnosis and prognosis of mental disorders[2]. Chatbots powered by large language models show promising results in mental health treatment and clinical decision support, although further research is still needed to integrate them into routine care[2]. The main challenges are to verify the effectiveness, scalability, and generalizability of the methods in different workplaces, as well as to address ethical, security, and privacy issues[2]. Mental health presents unique challenges compared to other medical fields, including the absence of objective biomarkers and reliance on behavioral and emotional assessments[2]. Successful introduction of AI into clinical practice will require robust validation and multi-site generalization of promising applications in real-world settings[2].