Artificial intelligence in mental health: A narrative review of applications, benefits, and ethical challenges

Abstract

Artificial intelligence (AI) is transforming mental health care by enabling novel approaches to assessment, outcome prediction, and treatment. However, the rapid growth of AI tools has outpaced evidence synthesis on their realworld clinical value, implementation barriers, and ethical risks, leaving clinicians and policymakers without clear guidance for responsible integration. This narrative review aimed to examine current AI applications in mental health care, identify their clinical benefits, implementation barriers, and ethical challenges, and define conditions for responsible integration that preserve the patient-clinician relationship. A structured literature search of PubMed, PsycINFO, and Scopus was conducted for peer reviewed English language articles published between January 2019 and March 2026. Studies addressing AI applications, benefits, implementation, or ethics in mental health were included and synthesised narratively into thematic categories. The review found that AI tools—for example, selfreferral chatbots that reduced waiting times and increased treatment uptake—provide expanded 24/7 access, improved clinical efficiency, and potential for individualised personalisation. Predictive models showed promise for treatment selection and risk stratification, while natural language processing unlocked unstructured clinical data. However, concerns included patient data privacy, algorithmic bias that may worsen existing inequities, potential erosion of the therapeutic relationship, and mixed acceptance among clinicians and patients. Many AI applications remain experimental, and regulatory frameworks have not kept pace with technological developments. This review did not include formal quality appraisal or quantitative synthesis; the evidence is limited by short followup periods, high dropout in chatbot trials, and a predominance of studies from highincome countries. Future research should employ longitudinal, codesigned, mixedmethods designs and pragmatic trials that evaluate clinical outcomes alongside equity, user trust, and the preservation of empathic, humanled care—rather than relying solely on uncontrolled implementation studies. AI should be responsibly integrated to augment, not replace, the clinical workforce. Successful application requires a balance between technological advancement, patient protection, and preservation of the patient-clinician relationship.