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

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

Responsible and innovative AI for mental health care: five priority themes

Artificial intelligence (AI) has entered psychiatry at scale, yet its clinical impact remains constrained by a sizable gap between technical validation and real-world implementation. The central barriers are no longer computational, but infrastructural: unreliable measurement systems, incomplete governance frameworks, and insufficient standards for clinical evidence and integration. This paper synthesizes insights from a 2026 American College of Neuropsychopharmacology (ACNP) study group examining how to responsibly translate AI into clinical mental health care. Building on this perspective, we outline five priorities required for clinical impact. First, robust measurement and phenotyping infrastructure, such as reliable psychometrics, digital phenotyping, and standardized data pipelines, is essential for clinically meaningful AI. Second, the most immediate and scalable impact of AI lies in clinician-facing augmentation tools that reduce workflow burden, such as ambient documentation systems and structured decision-support pipelines, with important research to conduct here. Third, patient-facing AI interventions show promise but require rigorous safety evaluation, particularly for implicit suicide risk and heterogeneous treatment effects. Fourth, governance and equity frameworks must extend beyond privacy to address bias, digital literacy, and research integrity. Fifth, future progress requires moving beyond predictive models toward causal, mechanistic approaches to precision psychiatry that better inform treatment decisions and clinical action. Together, these priorities define a translational agenda for 2026 and beyond: AI in mental health will succeed not through model performance alone, but through disciplined integration into clinical workflows, measurement systems, and governance structures that ensure safety, equity, and real-world effectiveness. This paper synthesizes insights from a 2026 American College of Neuropsychopharmacology (ACNP) study group examining how to responsibly translate AI into clinical mental health care. Building on this perspective, we outline five priorities required for clinical impact. These priorities define a translational agenda for 2026 and beyond: AI in mental health will succeed not through model performance alone, but through disciplined integration into clinical workflows, measurement systems, and governance structures that ensure safety, equity, and real-world effectiveness.

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

Artificial intelligence has been proposed as a corrective to three persistent problems in mental healthcare: diagnostic imprecision, the trial-and-error character of treatment selection, and the episodic nature of clinical monitoring. The volume of primary research has expanded rapidly, yet few tools have altered routine practice. This critical narrative review examines evidence across the three domains in which artificial intelligence has been most extensively applied to mental health, namely diagnostic classification and risk detection, treatment personalisation, and continuous patient monitoring, and asks why demonstrated technical performance has so rarely converted into demonstrated clinical benefit. Literature was identified through a bibliographic metadata registry, a biomedical citation index, targeted searching of scholarly and institutional sources, and backward and forward citation tracking, covering January 2015 to 11 June 2026, with earlier work retained where conceptually necessary. Evidence was appraised for design adequacy, validation strategy, sample representativeness, outcome definition and reporting transparency, then synthesised thematically rather than study by study. Three findings recur. Apparent accuracy is systematically inflated by internal validation, small and selected samples, and reference standards of limited reliability; where external validation has been attempted, discrimination frequently falls towards chance. The three domains differ markedly in evidential maturity, since monitoring and conversational intervention now rest on randomised evidence and pooled effect estimates, whereas diagnostic classification and treatment-response prediction remain largely at the model-development stage. The binding constraints on translation are infrastructural and epistemic rather than algorithmic, encompassing narrow training populations, unreliable outcome labels, absent prospective evaluation and immature governance. Unresolved questions include whether any model confers benefit over routine care in prospective use, how algorithmic outputs should enter clinical judgement, and how safety should be established for generative systems operating outside professional supervision. Progress will depend less on model refinement than on representative longitudinal datasets, standardised outcome definitions, prospective impact evaluation and governance capable of distinguishing wellness products from clinical instruments.

An Umbrella Review of Artificial Intelligence Applications in Mental Health Care.

Artificial intelligence has significant potential to enhance mental health nursing by supporting early identification of symptoms, improving access to care and strengthening clinical decision-making, with stronger evidence observed in reviews evaluating AI interventions for early detection and risk prediction.

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.

Explainable Artificial Intelligence for Tabular Data in Healthcare: A Systematic Review of Methods, Evaluation, and Applications

This systematic review provides a comprehensive analysis of XAI methods specifically applied to tabular healthcare data for classification tasks, revealing that SHAP remains the dominant post-hoc method, achieving strong model fidelity but showing inconsistent alignment with clinical expert reasoning.