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

arXiv: https://arxiv.org/abs/2603.16204 · HTML: https://mlsanity.com/paper/2603.16204/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 103 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [The opportunities and risks of large language models in mental health](https://mlsanity.com/paper/2403.14814/) (2024)
- [Remote patient monitoring using artificial intelligence: Current state, applications, and challenges](https://mlsanity.com/paper/2301.10009/) (2023)
- [Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamics](https://mlsanity.com/paper/2303.09601/) (2023)
- [The Significance of Machine Learning in Clinical Disease Diagnosis: A Review](https://mlsanity.com/paper/2310.16978/) (2023)

## Built on by (most cited first, 1 of 1)

- [SAGE: A Strategy-Aware Graph-Enhanced Generation Framework For Online Counseling](https://mlsanity.com/paper/2604.26630/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI)-enabled digital interventions, including Generative AI (GenAI) and Human-Centered AI (HCAI), are increasingly used to expand access to digital psychiatry and mental health care. This PRISMA-ScR scoping review maps the landscape of AI-driven mental health (mHealth) technologies across five critical phases: pre-treatment (screening/triage), treatment (therapeutic support), post-treatment (remote patient monitoring), clinical education, and population-level prevention. We synthesized 36 empirical studies implemented through early 2024, focusing on Large Language Models (LLMs), machine learning (ML) models, and autonomous conversational agents. Key use cases involve referral triage, empathic communication enhancement, and AI-assisted psychotherapy delivered via chatbots and voice agents. While benefits include reduced wait times and increased patient engagement, we address recurring challenges like algorithmic bias, data privacy, and human-AI collaboration barriers. By introducing a novel four-pillar framework, this review provides a comprehensive roadmap for AI-augmented mental health care, offering actionable insights for researchers, clinicians, and policymakers to develop safe, effective, and equitable digital health interventions.