Aug 2026· Journal of Society Counseling· 0 citations· 34 references
TL;DR
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.
Abstract
Mental illness represents a growing global health concern, with increasing prevalence and burden across populations. Timely diagnosis and effective treatment are essential to improving individual health outcomes. In this context, artificial intelligence (AI) offers significant potential through rapid and precise data analysis. This narrative literature review aims to examine the application of AI technologies, specifically natural language processing (NLP), machine learning (ML), and predictive modelling, in the diagnosis and therapy of mental health disorders among adult clinical populations. The review includes twelve peer-reviewed articles indexed in Scopus between 2014 and 2024, selected based on relevance, methodological rigor, and contribution to the field. Findings were organised into three thematic clusters: (1) AI-assisted diagnostic accuracy and predictive modelling (e.g., fMRI-based PTSD prediction, depression detection via multimodal neural networks); (2) AI-enhanced therapeutic delivery and user engagement (e.g., AI-assisted online social therapy, remotely supervised brain stimulation); and (3) ethical, privacy, and implementation challenges (e.g., data bias, lack of transparency, and population representativeness). 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. Future research should focus on developing standardised implementation protocols using frameworks such as PRISMA, ensuring population diversity, and addressing ethical safeguards. Collaboration among mental health professionals, AI technologists, and policymakers is essential to promote safe, effective, and equitable integration of AI in psychological diagnosis and therapy.
Evidence suggests that while AI tools can temporarily reduce symptoms and improve accessibility to professional help for mild to moderate conditions, they are less effective in cases of severe or complex disorders.
Nan-Xi Zhang· Theoretical and Natural Scie...· 0 citations
This chapter explores how AI talks, listens, and helps people living with SMIs, examining the nature and limitations of AI in transforming the diagnosis and treatment of SMIs.
Leelawati Pokhrel, Mohd Arsalan, Reeta Parmar et al.· Engineering & Technology· 0 citations
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.
Oyebode Mary Oluwabunmi, Anyebe Daniel Ameh, Jacob Miracle Godswill et al.· Journal of medicine and heal...· 0 citations
The International Workshop on AI for Cognitive and Mental Health Support is proposed, a half-day interdisciplinary forum that brings together researchers and practitioners from data mining, machine learning, NLP, NLP, HCI, healthcare, and social sciences to advance trustworthy, effective, and socially responsible AI solutions for cognitive and mental health support.
Xiangmeng Wang, Haoyang Li, Chen Li et al.· Proceedings of the 32nd ACM...· 0 citations
Artificial intelligence (AI) has become increasingly prominent in psychiatric research and clinical practice, offering new approaches to diagnosis, risk stratification, and personalised treatment planning. Advances in machine learning, digital phenotyping, and multimodal data integration have enabled tools capable of analysing complex behavioural, clinical, and neurobiological information. This review synthesises current developments in AI based psychiatric applications, examining diagnostic innovations, predictive modelling, and emerging treatment personalisation strategies. While reported accuracies and predictive performance are encouraging, the field remains constrained by methodological variability, limited external validation, and challenges related to transparency, ethics, and clinical implementation. Future progress will depend on rigorous validation, harmonised reporting standards, and integration of AI systems into real world clinical workflows.
A. Shishodia· Journal of Psychiatry and Ps...· 0 citations
AI is significantly impacting the way that mental health diagnostic tools are developed through their ability to
provide affordable, accessible and efficient means of detecting psychological disorders like depression. While the current
state of screening includes many effective tools (i.e., Clinical Interviews and Self- Reported Questionnaires) they have some
inherent shortcomings; these include, but are not limited to, being subjective and/or delayed diagnoses, lack of access to
individuals who may be experiencing difficulties with their mental health, and dependency on the availability of
professional interventions. The shortfalls identified above demonstrate the need for the development of intelligent systems,
capable of conducting rapid and accurate evaluations of an individualʼs mental health. An AI-driven Depression Level
Prediction System was therefore created to collect structured clinical information, as well as unstructured textual input in
order to create a full and complete assessment of an individualʼs mental health condition. Utilizing the PHQ 9 survey
instrument as the basis for collecting clinical information, the system utilizes Natural Language Processing techniques to
evaluate user-generated text, thereby gaining further insight into an individualʼs emotional and psychological trends.
The system described herein utilizes three machine learning-based predictive models Random Forest, SVM,
XGBoost) to predict an individualʼs level of depression as one of four categories (minimal, mild, moderate or severe).
Unlike prior binary prediction models utilized in the context of mental health evaluations, the described model
provides fine-tuned evaluations that can be more effectively used in practical applications of mental health
monitoring. Additionally, Explainable AI techniques were incorporated into the design of the system to improve
transparency and interpretability of the results produced by the system. Such capabilities enable both patients and
clinicians to identify specific variables within the results that contributed to the systemʼs predictions. The modular nature
of the system enables scalability, flexibility and efficient operation of the system even when utilizing lightweight
hardware that does not require extensive computing capabilities. Experimental validation demonstrated that the described
system achieved greater accuracy and better generalization than other systems currently available. Through its ability to
process both behavioral inputs, questionnaire responses and textual sentiment analysis, the system offers a holistic view
of an individualʼs mental health status. Beyond improving early detection, the described system can assist clinicians and
patients in making informed decisions regarding treatment options for issues related to mental health. Therefore, the
system serves as a connection between traditional healthcare practices and emerging AI technology to provide a private
and secure method for evaluating mental health conditions.
Danda Shruthi, Annamaneni Sai, Kalal Taruni et al.· International Journal of Inn...· 0 citations
