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Mental health disorders contribute significantly to the global burden of disease, affecting quality of life and causing disability. These challenges are compounded by inequitable access to timely and effective mental health services, particularly in low-resource settings. Recently, artificial intelligence (AI) has emerged as a transformative tool in mental healthcare, offering novel approaches to enhance diagnosis, personalize treatment, and support continuous patient monitoring. This review explores the current landscape of non-generative AI applications in mental health, focusing on core methodologies such as machine learning, deep learning, and natural language processing. These techniques show promise in improving diagnostic accuracy, enabling adaptive and scalable digital therapy delivery systems, facilitating real-time mental health risk prediction through the analysis of multimodal data. According to our study, the majority of research demonstrated increased therapy personalization and diagnostic accuracy; however, significant challenges still exist due to low dataset diversity, algorithmic bias, and a lack of clinical validation. Ethical considerations and the need for transparent, explainable, and clinician-trustworthy AI are increasingly recognized as critical to successful implementation. Overall, AI-driven methods have strong potential to improve accessibility and effectiveness in mental health treatment, provided future studies prioritize equity, interpretability, and clinical relevance. We ran a narrative review between January 2019 to June 2025, screened in duplicate, and used thematic synthesis across diagnosis, therapy support, and monitoring.
Ali et al. (Thu,) studied this question.