A Scoping Review of AI-Driven Digital Interventions in Mental Health Care: Mapping Applications Across Screening, Support, Monitoring, Prevention, and Clinical Education
Organizations: School of International and Public Affairs Columbia University New York · Department of Psychology Seton Hall University South Orange
Abstract
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.
Figures & tables
| Label | Theme |
| AI Chatbots | Rule-based or scripted dialogue systems that deliver predefined psychoeducation or CBT prompts; no statistical/ML adaptation; always user-interfacing. |
| Conversational AI Agents | Multi-turn dialogue systems that incorporate traditional NLP components, such as intent classification or sentiment analysis to tailor replies, but do not employ large language model generation. |
| Machine Learning (ML) Models | Supervised algorithms (e.g., logistic regression, SVM, and gradient boosting) trained on structured or text features to classify diagnosis, predict outcomes, or service flow, generally backend analytics. |
| Natural Language Processing (NLP) Tools | Standalone language-processing pipelines, tokenization, topic modelling, and emotion detection, used either to support a conversational or analyze text corpora; excludes LLMs. |
| Large Language Models (LLMs) | Transformer-based generative models with >1 billion parameters (e.g., GPT-3.5/4) capable of free-text generation, contextual memory, and zero-shot reasoning; typically fine-tuned for multi-turn counselling. |
| Deep Learning (DL) Models | Neural networks such as CNNs or RNNs applied to non-text signals (images, sensor streams) or structured clinical data for pattern recognition and outcome prediction. |
| Clinical Phase/Scenario | Descriptions |
| Pre-treatment/Screening | Interventions used before formal care begins, including online self-referral, triage, or risk screening. |
| Treatment | AI components integrated during active psychotherapy, pharmacotherapy, or combined treatment phases. |
| Post-treatment/Monitoring | AI tools used for follow-up care, symptom monitoring, risk assessment, or treatment adjustment after formal treatment. |
| General support and prevention | Standalone tools aimed at maintaining well-being, reducing stress, or preventing mental health problems in non-clinical or community populations. |
| Clinical education | AI tools used to train, assess, or upskill mental health professionals, clinical students, or educators. |
| Functional Category | Descriptions |
| Reference | Scenario/Application | AI Technology | Purpose | Main Result |
| [ 21 ] | Mental health (MH) assessment | AI chatbot | Examined “Limbic Access” AI in enhancing mental illness recovery in NHS services. | The use of AI tool was associated with an increase in recovery rates from 47.1% in the pre-implementation period to 48.9% post-implementation. |
| [ 23 ] | Patient monitoring | AI prediction modelling | AI-based RPM model incorporates RFID for monitoring mental health, targeting vital signs and activity classification. | The implementation can help to monitor patients with mental illnesses sufficiently and effectively support the treatment teams to provide timely interventions, improve patient safety, and prevent incidents such as self-harm. |
| [ 31 ] | Treatment outcomes prediction | Machine learning (ML), RNN | AI applications in iCBT predict mental health outcomes. | The developed AI models demonstrated good accuracy in predicting patient outcomes, resulting in approximately 87% accuracy after three clinical reviews. |
| [ 32 ] | Detects and diagnoses | ML | AI tool improves MHM assessment, using fewer questions while maintaining diagnostic precision. | The system provided an accuracy level of 89% in diagnosing mental disorders with only 28 questions asked during diagnostic sessions, reducing questions volume, and encouraging better participation. |
| [ 33 ] | MH counseling/therapy | AI chatbot | AI chatbots’ emotional interactions enhance user satisfaction and retention. | Emotional disclosure in chatbot significantly increases satisfaction and reuse intention. Users’ emotional disclosure intention and the perceived intimacy with a chatbot also mediate the effect. |
| [ 34 ] | Peer support | Conversational AI agent | HAILEY AI system fosters empathy in text-based peer mental health support. | The study showed a substantial growth of users’ empathic response due to the human–AI collaboration. |
| [ 42 ] | MH monitoring | Deep learning model | AI-edge computing system monitors negative info’s impact on pandemic-related mental health. | The system is capable of measuring negative information and its influence on well-being, offering an effective strategy for mental health monitoring during pandemic. |
| [ 22 ] | MH treatment | ML | Assessed the feasibility of recommender systems in personalizing treatment within digital mental health therapy. | Suggested that recommender systems could successfully individualize therapeutic recommendations and improve outcomes. |
| [ 43 ] | Diagnosis, treatment outcomes prediction | Deep learning model | M2C model in art therapy accurately predicts stress levels through art analysis. | The M2C model had 88.5% accuracy in predicting stress levels based on the data from offered art psychotherapy tests. |
| [ 44 ] | MH counseling/therapy | AI chatbot | AI-driven counselor’s artificial empathy compared with human counselors. | AI-based empathetic counseling was rated with less helpfulness. |
| [ 45 ] | Clinical education | ChatGPT | ChatGPT’s effectiveness in simulating client interactions for counseling practice assessed. | Suggested that while ChatGPT can simulate various aspects of client interactions, it has limitations, such as lack of non-verbal cues. |
| [ 46 ] | Diagnosis, MH support | ChatGPT | ChatGPT’s emotional understanding in conversations evaluated using LEAS. | ChatGPT’s emotional awareness was significantly higher compared to human norms, with improvement over time. It achieved high accuracy in fitting emotions to scenarios. |
| [ 26 ] | MH treatment | ChatGPT | ChatGPT’s response adaptability for BPD and SPD conditions analyzed. | For BPD, ChatGPT showed the ability to respond with a higher emotional resonance than SPD, which suggests its potential use as personalized mental health intervention. |
Mental Health Counseling
Mental health counseling studies how people and AI systems provide, evaluate, and improve psychological support through therapeutic conversations.
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