Artificial Intelligence in Mental Healthcare: A Critical Narrative Review of Diagnosis, Treatment Personalisation and Patient Monitoring

Artificial Intelligence in Mental Healthcare: A Scoping Review of Clinical Applications, Evidence, Safety, and Equity

Artificial Intelligence in Primary Care for Disease Surveillance, Early Diagnosis, Preventive Care and Population Health Outcomes: A Critical Narrative Review

Primary care generates the longitudinal, population-wide records on which much contemporary health intelligence depends, and it is the setting in which most disease is first suspected and most preventive activity is delivered. Artificial intelligence has accordingly been proposed as a means of strengthening disease surveillance, accelerating early diagnosis, extending preventive care and improving population health outcomes. This critical narrative review examines whether the accumulated evidence supports those expectations, and where it does not. Peer-reviewed literature was identified through Europe PMC, which indexes MEDLINE and PubMed Central records, and through Crossref metadata records, supplemented by citation-informed retrieval and by targeted searching of an intergovernmental publication repository. Evidence was appraised for design adequacy, validation status, setting representativeness and the nature of the outcomes measured. The synthesis identifies a consistent and analytically important asymmetry across the four domains. Diagnostic performance evidence is comparatively mature for image-based and physiological-signal tasks, several of which have been evaluated prospectively or in randomised designs conducted wholly or partly in primary care. Surveillance and preventive applications remain dominated by retrospective model development, with external validation uncommon and prospective impact evaluation rare. Population health outcomes are almost never measured directly; the literature substitutes discrimination statistics, detection yield and process indicators, and the causal chain linking improved prediction to improved population health remains largely untested. Recurrent methodological weaknesses, including case-control sampling, optimistic internal validation, incomplete reporting of population characteristics and the use of convenient proxy outcomes, plausibly explain part of the gap between reported accuracy and demonstrated benefit. Evidence on differential performance across population subgroups is sparse relative to the strength of equity claims made for these technologies. Progress will depend less on further gains in discrimination than on pragmatic evaluation in representative primary care populations, on prespecified patient-relevant and population-level endpoints, and on governance that treats deployed models as interventions requiring continued surveillance rather than as fixed devices.

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

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.

Artificial Intelligence in Mental Health: A Structured Review of Current Evidence across the Care Pathway

It is argued that AI should be framed as an augmentation of - not a replacement for - the clinical relationship, with equity, consent and explainability treated as first-order design constraints.

Review of Artificial Intelligence Applications in Mental Health Diagnosis and Therapy

These technologies show promise in reducing human error and enhancing mental health care delivery; however, persistent challenges include data privacy, ethical considerations, and the need for diverse, large-scale datasets.

Artificial Intelligence in Psychiatry: Five Decades of Progress and Persistent Translational Challenges

The clinical impact of psychiatric AI will likely depend less on algorithmic novelty alone than on clearer clinical targets, prospective validation, implementation trials, patient‐centered evaluation, equity‐sensitive generalizability, and mental health–specific governance.