The review presents a comprehensive and well-structured synthesis of current developments in AI applications within mental healthcare. It effectively highlights a broad spectrum of tools and approaches, from diagnostic algorithms to virtual therapy platforms, positioning AI as a transformative force in the field. However, a few critical observations may further guide scholarly reflection and future work:
i. Methodological Specificity: While the inclusion of 92 studies demonstrates extensive coverage, the lack of a detailed quality assessment framework for evaluating those sources limits the interpretative strength of the conclusions. Future reviews could benefit from more explicit methodological rigor in evaluating study validity and potential bias.
ii. Analytical Depth: The review emphasizes breadth over depth. Discussions on model validation, performance metrics, and empirical evidence behind widely used platforms (e.g., Woebot, Kintsugi) would benefit from more critical appraisal, especially given the clinical sensitivity of mental health interventions.
iii. Clinical and Operational Challenges: Although the paper outlines the promise of AI integration, it gives limited attention to implementation challenges in real-world mental health systems—such as regulatory compliance, clinician acceptance, integration with EHRs, and equity in access.
iv. Ethical and Cultural Dimensions: Ethical concerns such as algorithmic bias and data security are mentioned but not deeply explored across cultural or systemic contexts. Greater attention to these variations would improve the generalizability and applicability of the review’s insights.
v. Human-AI Dynamics: The discussion on preserving the therapeutic alliance amid AI integration is relevant, yet lacks engagement with existing models or empirical studies on human-AI interaction in clinical settings.
vi. Future Research Directions: The concluding remarks rightly emphasize the need for regulation and ongoing R&D but could be strengthened by more actionable guidance, particularly on interdisciplinary collaboration, longitudinal evaluation, and mechanisms for ensuring model transparency.