ARTIFICIAL INTELLIGENCE IN MENTAL HEALTH SERVICES: CURRENT APPLICATIONS, CHALLENGES, AND FUTURE DIRECTIONS

Abstract

Background: Artificial intelligence (AI) is increasingly integrated into mental health care, offering tools for assessment, monitoring, risk prediction, and intervention. Rising global mental health needs, clinician shortages, and advances in digital technologies have accelerated adoption of conversational agents, digital phenotyping, clinical decision-support systems, and large language models (LLMs). Despite substantial promise, concerns remain regarding bias, transparency, safety, and real-world effectiveness. Methods: This narrative review synthesized peer-reviewed studies published between 2017 and early 2025. Searches were conducted in PubMed, PsycINFO, Scopus, and Google Scholar. Eligible sources included randomized controlled trials, systematic reviews, meta-analyses, observational studies, and major policy documents evaluating AI for mental health diagnosis, monitoring, intervention, or clinical decision support. Results: Findings across more than 120 studies show that AI-based conversational agents provide modest but consistent improvements in symptoms of mild to moderate depression and anxiety. Diagnostic models and triage tools demonstrate potential for identifying psychosis risk, suicide risk, and treatment response, but external validity remains limited by dataset bias and variable performance in real-world settings. Digital phenotyping offers early-warning capabilities for relapse, while LLMs improve documentation efficiency but struggle with crisis detection and safety-sensitive reasoning. Ethical concerns—particularly relating to privacy, informed consent, explainability, and algorithmic fairness—remain widespread. Conclusions: AI has significant potential to enhance mental health care through scalable interventions, improved diagnostic accuracy, and proactive monitoring. However, safe integration requires robust governance, transparency, and sustained human oversight. Future progress depends on large-scale clinical trials, bias mitigation, standardized evaluation frameworks, and the development of equitable hybrid human-AI care models.

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