Practical Implementation of Artificial Intelligence in Mental

Healthcare: An Integrative Review of Approaches and Case

Studies

Nureize bt Arbaiy1,* Massila Kamalrudin 2

1 Fakulti Sains Komputer dan Teknologi Maklumat,University Tun Hussein Onn Malaysia (UTHM)86400 Batu Pahat, Johor,

Malaysia 2Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Malaysia

Received: January 24, 2026 Revised: February 22, 2026 Accepted: April 19, 2026 ⋆ Corresponding author

ABSTRACT

Artificial Intelligence in mental healthcare is a revolution in the delivery, evaluation and administration of mental

health services. This review also maps current literature covering the practical application of AI in mental health

practice, focusing on the descriptive use and specific integration and case analyses of those technologies in context.

AI solutions are as follows: Solutions that assist with the diagnosis of psychiatric patients, as well as solutions

enabled by artificial Intelligence that foretell situations of severe mental health emergencies of individual citizens.

Some good examples, Like the REACH VET program, show how AI, using EHRs, can identify suicidal veterans’

risk and prevent potential suicides. Nevertheless, numerous challenges exist when using AI in mental health, such as

workers’ resistance, ethical questions about patient data, and clinician engagement. Concerning implementation

methodology, this review has incorporated ideas from implementation science, showing the need to employ a guided

approach when implementing AI technologies in clinical practice. Based on the study results, there is potential

for increasing patient outcomes through artificial Intelligence and group-specific treatment options, better tools for

diagnosing diseases and practical cooperation, but constant work of technologists, clinicians, and policymakers will

be needed to eliminate existing issues and ensure equal access to innovations in the sphere of mental health.

Keywords: Artificial Intelligence Healthcare Diagnostics Personalized Medicine Interdisciplinary Collaboration

1. INTRODUCTION

AI technologies are constantly evolving, and they have successfully

entered many spheres of human life. Their increasing

use has encouraged further consideration of how these

technologies may be applied in mental healthcare. Mental

health disorders represent a significant and growing burden

on healthcare systems worldwide. In this context, AI offers

unique approaches to diagnosis, treatment, monitoring, and

early intervention [1].

To begin with, the use of AI in mental healthcare serves

several key objectives, particularly addressing the shortage

of mental health professionals and improving the timeliness

and accuracy of diagnosis. AI can support clinicians through

machine learning algorithms, predictive analytics, and natural

language processing, allowing healthcare providers to process

large volumes of clinical and behavioral data more efficiently

[2].

This review paper focuses on the practical applications of AI

in mental health. It examines various real-life deployments of

AI technologies, including their role in supporting differential

diagnosis of psychiatric conditions, predicting the likely