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This scoping review analyzes 36 empirical studies to map the applications of AI-driven digital interventions across five phases of mental healthcare: screening, support, monitoring, prevention, and clinical education. The review identifies chatbots, NLP tools, and ML/DL models as the most common AI modalities, primarily used for support, monitoring, and self-management. It introduces a four-pillar framework to guide the development of safe, effective, and equitable digital mental health interventions, while also highlighting challenges like algorithmic bias and data privacy.
AI in mental healthcare is more hype than help: current interventions primarily augment existing processes like monitoring and support, rather than providing standalone treatments.
Background/Objectives: Artificial intelligence (AI)-enabled digital interventions are increasingly used to expand access to mental health care. This PRISMA-ScR scoping review maps how AI technologies support mental health care across five phases: pre-treatment (screening), treatment (therapeutic support), post-treatment (monitoring), clinical education, and population-level prevention. Methods: We synthesized findings from 36 empirical studies published through January 2024 that implemented AI-driven digital tools, including large language models (LLMs), machine learning (ML) models, and conversational agents. Use cases include referral triage, remote patient monitoring, empathic communication enhancement, and AI-assisted psychotherapy delivered via chatbots and voice agents. Results: Across the 36 included studies, the most common AI modalities included chatbots, natural language processing tools, machine learning and deep learning models, and large language model-based agents. These technologies were predominantly used for support, monitoring, and self-management purposes rather than as standalone treatments. Reported benefits included reduced wait times, increased engagement, and improved symptom tracking. However, recurring challenges such as algorithmic bias, data privacy risks, and workflow integration barriers highlight the need for ethical design and human oversight. Conclusion: By introducing a four-pillar framework, this review offers a comprehensive overview of current applications and future directions in AI-augmented mental health care. It aims to guide researchers, clinicians, and policymakers in developing safe, effective, and equitable digital mental health interventions.