ReviewAustralian journal of psychology2026
From predictive algorithms to generative intelligence: a decadal bibliometric mapping of global research frontiers in AI-driven mental health (2016-2025).
Review in Australian journal of psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The escalating global mental health burden has triggered an urgent real-world need for scalable artificial intelligence (AI) solutions. This study employs a quantitative bibliometric approach to objectively present the research hotspots and emerging trends in the global AI-driven mental health landscape (2016-2025), specifically capturing the post-2023 surge in generative AI. Methods: We analyzed 9,623 original research articles from Web of Science and Scopus. CiteSpace and VOSviewer were utilized for co-authorship analysis, literature co-citation clustering, and keyword burst detection. Results: Research output grew exponentially, surpassing 3,000 publications in 2025. The US and China led productivity, with King's College London as the top institution. Research clusters encompass foundational AI methodologies and four clinical domains: diagnosis (e.g., neuroimaging), digital interventions (e.g., chatbots), targeted conditions (e.g., PTSD), and suicide prevention. Recent keyword bursts highlight diagnostic accuracy, loneliness, and occupational burnout as post-2023 frontiers. Conclusion: The field is transitioning from exploratory algorithmic development to patient-centered clinical applications. While AI offers transformative potential for mental health research and care, future research must urgently prioritize patient safety, algorithmic bias, data privacy, safeguards against algorithmic hallucinations, and ethical integration into clinical workflows to ensure sustainable implementation.
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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.