Artificial Intelligence and Mental Health: Advances, Applications, Challenges and Future Directions-A Narrative Review

Abstract

Artificial Intelligence (AI) is increasingly influencing the delivery, assessment, and organization of mental health care. Recent advances in machine learning, natural language processing, deep learning, large language models, generative artificial intelligence, digital phenotyping, and conversational agents have expanded the potential role of AI beyond conventional decision-support systems. Applications now include early identification of psychological distress, prediction of suicide risk, clinical decision support, personalized psychoeducation, digital psychotherapy, symptom monitoring, relapse prediction, mental health promotion, and professional education. Emerging evidence suggests that AI-supported conversational interventions can improve selected symptoms of depression and anxiety and may enhance mental health literacy and self-care behaviors. Recent randomized trials of generative AI-based interventions have provided encouraging evidence regarding feasibility, engagement, therapeutic alliance, and symptom improvement. However, the evidence remains heterogeneous, and important concerns exist regarding hallucination, inappropriate reassurance, algorithmic bias, privacy, cybersecurity, informed consent, emotional dependency, accountability, and the management of high-risk clinical situations. The World Health Organization has emphasized that AI for health should be developed within frameworks of safety, transparency, equity, human autonomy, and accountability. The future of AI in mental health is therefore unlikely to involve replacing mental health professionals; rather, its greatest value may emerge from carefully governed human–AI collaboration. This narrative review examines recent advances in AI-based mental health care, current evidence, clinical applications, ethical concerns, implications for nursing and multidisciplinary practice, and priorities for future research.