Artificial intelligence as a predictive tool for mental health status: Insights from a systematic review and meta-analysis

Arsalan Humayun, Ashwini M Madawana, Akram Hassan, Al Mahmud, Noorshaida Kamaruddin, Syed Husni Noor, Syed Hatim Noor and Mohamad Arif Awang Nawi

PLOS ONE, 2025, vol. 20, issue 9, 1-14

Abstract: This systematic review and meta-analysis evaluates the effectiveness of AI-driven tools, particularly conversational agents (CAs), in alleviating psychological distress and improving mental health outcomes. The focus is on their impact across diverse populations, including clinical, subclinical, and older adults. A comprehensive search was conducted in PubMed, Google Scholar, Elsevier, and Scopus using specific MeSH terms and keywords such as “Artificial Intelligence,” “Machine Learning,” “Natural Language Processing,” “Depression,” and “Anxiety.” The timeframe included studies published between January 2000 and July 2024. Inclusion criteria comprised peer-reviewed original research articles, cohort studies, and case reports focusing on AI tools for mental health. Systematic reviews, secondary sources, and non-English publications were excluded. Random-effects meta-analysis was conducted using standardized mean differences, with effect sizes synthesized in forest plots. Twenty studies were included in the qualitative synthesis and six in the quantitative meta-analysis. The analysis demonstrated that AI-based CAs significantly reduce anxiety (Cohen’s d = 0.62, p

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0332207

DOI: 10.1371/journal.pone.0332207

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