From predictive algorithms to generative intelligence: a decadal bibliometric mapping of global research frontiers in AI-driven mental health (2016-2025)

07/08/2026

AI Summary

Exponential rise in AI-driven mental health research through 2016–2025, exceeding 3,000 publications in 2025; US and China lead, King's College London top institution.
Research clusters span foundational AI methods and four clinical domains: diagnosis (neuroimaging), digital interventions (chatbots), targeted conditions (PTSD) and suicide prevention.
Future priorities: patient safety, algorithmic bias mitigation, data privacy, safeguards against algorithmic hallucinations, and ethical integration into clinical workflows.

AI Summary

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.

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.

Basic summary

Aust J Psychol. 2026 Aug 5;78(1):2710168. doi: 10.1080/00049530.2026.2710168. eCollection 2026.

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.

PMID:42564719 | PMC:PMC13446058 | DOI:10.1080/00049530.2026.2710168

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