Artificial Intelligence and Digital Mental Health: Emerging Innovations and Their Implications

A special issue of Healthcare (ISSN 2227-9032).

Deadline for manuscript submissions: 22 August 2026 | Viewed by 455

Special Issue Editor


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Guest Editor
Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR 999077, China
Interests: artificial intelligence and digital mental health; natural language processing for mental health; technology, electronics, and mental health; social withdrawal and psychological well-being
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Special Issue Information

Dear Colleagues, 

Artificial Intelligence (AI) is transforming mental health and psychotherapy by enhancing screening tools, diagnostic assessments, and treatment approaches. This research area is crucial as it bridges technology and mental health, offering valuable insights for more accurate assessments and personalized therapeutic interventions, which ultimately enhance mental health outcomes.

We are pleased to invite you to contribute evidence-based recommendations for clinicians and researchers regarding the use of AI in mental health and psychotherapy. We welcome both original articles and review papers that highlight how AI-powered tools can offer new perspectives in digital mental health and drive advancements in psychotherapy.

This Special Issue aims to explore the transformative impact of AI on mental health and psychotherapy practices. AI is reshaping assessment and intervention methods, bringing new perspectives to clinical decision-making. Advancements such as large language models, multimodal AI, and smart wearables are making mental health support more accessible and personalized, complementing traditional approaches. This issue encourages interdisciplinary research to fully realize AI’s potential while addressing associated risks, with a commitment to fostering innovation that upholds ethical standards.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Systematic reviews and meta-analyses examining AI applications in mental health and related fields;
  • Interventional studies evaluating the effectiveness of AI-based psychotherapy;
  • Observational studies investigating the use of AI in digital mental health.

I look forward to hearing from you.  

Dr. Tim M. H. Li
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • digital mental health
  • psychotherapy
  • assessment
  • intervention
  • precision medicine
  • evidence-based
  • AI regulation

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Published Papers (1 paper)

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Review

24 pages, 1260 KB  
Review
Safety Mechanisms and Risk Mitigation in Generative AI Mental Health Chatbots: A Systematic Scoping Review
by Lotenna Olisaeloka, Chris G. Richardson, Angel Y. Wang, Richard J. Munthali and Daniel V. Vigo
Healthcare 2026, 14(10), 1395; https://doi.org/10.3390/healthcare14101395 - 20 May 2026
Abstract
Background: Generative AI (GenAI) mental health chatbots are increasingly being developed to help address persistent barriers to mental healthcare. Unlike earlier rule-based and retrieval-based systems, GenAI chatbots generate open-ended outputs that can be inaccurate and unsafe. Documented harms from general-purpose GenAI chatbots have [...] Read more.
Background: Generative AI (GenAI) mental health chatbots are increasingly being developed to help address persistent barriers to mental healthcare. Unlike earlier rule-based and retrieval-based systems, GenAI chatbots generate open-ended outputs that can be inaccurate and unsafe. Documented harms from general-purpose GenAI chatbots have highlighted the need for purpose-built interventions with dedicated safeguards, yet how safety is implemented in such interventions remains poorly understood. Methods: This scoping review followed the Joanna Briggs Institute methodology and PRISMA-ScR guidelines, with a prospectively registered and peer-reviewed protocol. A systematic search of seven academic databases and search engines including MEDLINE, Scopus, PsycINFO, ACM Digital Library, IEEE Xplore, Google Scholar and Consensus was conducted in July 2025. Two reviewers independently screened records and extracted data. Safety mechanisms and risk mitigation strategies were narratively synthesised across three pre-specified domains: technical safeguards, pre-deployment safety considerations, and delivery-phase risk mitigation strategies. Results: Twenty-one studies across 11 countries were included. Most interventions incorporated at least one technical safety mechanism, most commonly fine-tuning and prompt engineering. A smaller subset implemented layered safety architectures combining retrieval systems, content filters or risk classifiers, and rule-based algorithms. Pre-deployment safeguards included clinical expert and user co-design approaches, research ethics procedures, and data privacy measures. During intervention delivery, detailed onboarding with role clarification was common, but human oversight was limited. Crisis referral protocols varied in rigour but were mostly underdeveloped, and systematic adverse event monitoring was sparse. Documented safety failures included missed suicidal ideation and provision of inaccurate clinical information. Conclusions: GenAI chatbot interventions require a robust sociotechnical approach that integrates technical safeguards with user co-design, procedural controls, and human oversight. Future research is needed to evaluate efficacy, improve safeguards and standardise safety outcome measurement. Regulatory oversight proportional to the risks these systems carry is required to enable integration into stepped or blended mental healthcare. Full article
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