ReviewDigital health
Network-based artificial intelligence in mental healthcare: A systematic review of chatbots, artificial intelligence/machine learning models and ethical considerations in global healthcare networks.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Fostering Digital Resilience Among Youth Who Experienced Cyberbullying: Design Thinking Approach.Journal of medical Internet research · 2026Article
- Perceived support from AI chatbots and loneliness among higher vocational college students: a short-term three-wave study of social interaction anxiety and perceived social support.Frontiers in psychology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This systematic review examines how artificial intelligence (AI), including machine learning (ML) models and AI-powered chatbots, contributes to the diagnosis, treatment and ethical governance of mental healthcare. It explores how AI-driven systems form interconnected healthcare networks that enhance accessibility, personalization and resilience of mental health services, aligning with the United Nations Sustainable Development Goal 3: Good Health and Well-Being. Methods: A comprehensive search across PubMed, IEEE Xplore and Google Scholar (2017-2024) was conducted using Boolean combinations of "AI," "machine learning," "chatbots" and "mental health." Screening followed Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines, yielding 37 high-quality studies for qualitative synthesis. Extracted data were categorized into three domains: (1) AI- and ML-based diagnostic models, (2) chatbot-enabled mental health support systems and (3) ethical and privacy considerations. Analytical dimensions included algorithmic performance, clinical outcomes, data governance and equity of access. Results: AI-driven interventions improved accessibility, diagnostic accuracy and therapeutic personalization. Chatbots such as Woebot, Wysa and Tess effectively reduced symptoms of depression and anxiety, increased user engagement and provided scalable support, particularly during the COVID-19 pandemic. ML models, including MentalBERT, MentalRoBERTa and SR-BERT, achieved F1 scores of 68-93% in mental health classification tasks. However, limitations included dataset bias, lack of longitudinal evidence and limited cross-cultural generalizability. Ethical analyses revealed persistent challenges concerning privacy, informed consent, algorithmic bias and accountability. Conclusion: AI technologies, when integrated with human oversight, offer transformative potential for global mental health systems by creating interconnected and adaptive care networks. These technologies can enhance efficiency, reduce barriers to care and support data-driven public health strategies. However, successful deployment depends on clear ethical frameworks that promote transparency, respect cultural contexts and preserve human oversight. Future research should prioritize longitudinal studies, inclusive datasets and ethical frameworks that maintain human-centered values in AI-enabled mental health systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.