A Scoping Review of AI-Driven Digital Interventions in Mental Health Care: Mapping Applications Across Screening, Support, Monitoring, Prevention, and Clinical Education

AI is expanding mental health care access across multiple stages of treatment: The review identifies AI applications throughout the entire patient journey—from initial screening and triage, through therapeutic support via chatbots, to ongoing remote monitoring and prevention programs—demonstrating that these technologies are being deployed to address bottlenecks at nearly every phase of mental health care delivery.

Chatbots and conversational agents show promise for reducing wait times and improving engagement: Key use cases include AI-powered referral triage systems and empathic communication tools that can provide immediate support, with documented benefits of reduced appointment delays and increased patient participation in their own care.

Algorithmic bias, data privacy, and trust remain critical unresolved challenges: Despite potential benefits, the review identifies recurring concerns about bias in AI systems, privacy risks with sensitive mental health data, and barriers to effective human-AI collaboration—suggesting that technical solutions alone are insufficient without addressing ethical and regulatory frameworks.

The field lacks standardized evaluation and real-world implementation data: By synthesizing only 36 empirical studies through early 2024, the review suggests that while AI mental health applications are proliferating, rigorous evidence about their safety and effectiveness in clinical practice remains limited, highlighting a gap between development and validated deployment.

A comprehensive framework is needed to guide responsible development: The authors propose a four-pillar framework to help researchers, clinicians, and policymakers navigate AI implementation in mental health, indicating that moving forward requires coordinated guidance rather than ad-hoc adoption of these technologies.