Artificial Intelligence Applications in Mental Health: A Systematic Review of Clinical Practice, Educational...

Background: Artificial intelligence (AI) is one of the most influential technological innovations in contemporary mental healthcare. Advances in machine learning, natural language processing, conversational agents, and large language models have accelerated the integration of AI into clinical practice, professional education, and healthcare. Despite its increasing adoption, important questions remain regarding its clinical effectiveness, implementation, safety, and ethical governance. Objective: This systematic review aimed to synthesize the current evidence on the application of artificial intelligence in mental health, with particular emphasis on clinical practice, educational transformation, and ethical governance. Methods: This systematic review was conducted in accordance with the PRISMA 2020 guidelines. PubMed/MEDLINE, Scopus, Web of Science, PsycINFO, and Google Scholar were systematically searched. The electronic database search was last conducted on 31 December 2025, and studies published between January 2019 and December 2025 were considered eligible. Eligible studies examined the application of artificial intelligence in mental health across clinical practice, educational contexts, and ethical governance. Study selection, data extraction, and methodological quality assessment were carried out independently by two reviewers using predefined eligibility criteria and standardized extraction forms. Owing to substantial methodological heterogeneity across the included studies, the findings were synthesized narratively. Results: A total of 88 studies met the eligibility criteria and were included in the final qualitative synthesis. The findings showed that AI demonstrated potential to improve diagnostic support, risk prediction, treatment planning, symptom monitoring, and access to psychological support. AI also supported educational innovation and workforce development while highlighting the importance of ethical governance for responsible implementation in mental healthcare. Conclusions: Future progress will depend on interdisciplinary collaboration to ensure that AI complements rather than replaces human expertise. Although AI demonstrates substantial potential, many systems remain experimental, with limited external validation. Prospective multicenter evaluation, transparent algorithm development, and robust ethical governance are therefore essential before widespread clinical implementation.