Artificial intelligence in mental healthcare: an overview and future perspectives

The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review

Artificial intelligence (AI) has been increasingly used in delivering mental healthcare worldwide. Within this context, the traditional role of mental health nurses has been changed and challenged by AI-powered cutting-edge technologies emerging in clinical practice. The aim of this integrative review is to identify and synthesise the evidence of AI-based applications with relevance for, and potential to enhance, mental health nursing practice. Five electronic databases (CINAHL, PubMed, PsycINFO, Web of Science and Scopus) were systematically searched. Seventy-eight studies were identified, critically appraised and synthesised following a comprehensive integrative approach. We found that AI applications with potential use in mental health nursing vary widely from machine learning algorithms to natural language processing, digital phenotyping, computer vision and conversational agents for assessing, diagnosing and treating mental health challenges. Five overarching themes were identified: assessment, identification, prediction, optimisation and perception reflecting the multiple levels of embedding AI-driven technologies in mental health nursing practice, and how patients and staff perceive the use of AI in clinical settings. We concluded that AI-driven technologies hold great potential for enhancing mental health nursing practice. However, humanistic approaches to mental healthcare may pose some challenges to effectively incorporating AI into mental health nursing. Meaningful conversations between mental health nurses, service users and AI developers should take place to shaping the co-creation of AI technologies to enhance care in a way that promotes person-centredness, empowerment and active participation.

Artificial intelligence in psychiatry: A systematic review and meta-analysis of diagnostic and therapeutic efficacy

Background

Artificial Intelligence (AI) has demonstrated significant potential in transforming psychiatric care by enhancing diagnostic accuracy and therapeutic interventions. Psychiatry faces challenges like overlapping symptoms, subjective diagnostic methods, and personalized treatment requirements. AI, with its advanced data-processing capabilities, offers innovative solutions to these complexities.

Aims

This study systematically reviewed and meta-analyzed the existing literature to evaluate AI's diagnostic accuracy and therapeutic efficacy in psychiatric care, focusing on various psychiatric disorders and AI technologies.

Methods

Adhering to PRISMA guidelines, the study included a comprehensive literature search across multiple databases. Empirical studies investigating AI applications in psychiatry, such as machine learning (ML), deep learning (DL), and hybrid models, were selected based on predefined inclusion criteria. The outcomes of interest were diagnostic accuracy and therapeutic efficacy. Statistical analysis employed fixed- and random-effects models, with subgroup and sensitivity analyses exploring the impact of AI methodologies and study designs.

Results

A total of 14 studies met the inclusion criteria, representing diverse AI applications in diagnosing and treating psychiatric disorders. The pooled diagnostic accuracy was 85% (95% CI: 80%-87%), with ML models achieving the highest accuracy, followed by hybrid and DL models. For therapeutic efficacy, the pooled effect size was 84% (95% CI: 82%-86%), with ML excelling in personalized treatment plans and symptom tracking. Moderate heterogeneity was observed, reflecting variability in study designs and populations. The risk of bias assessment indicated high methodological rigor in most studies, though challenges like algorithmic biases and data quality remain.

Conclusion

AI demonstrates robust diagnostic and therapeutic capabilities in psychiatry, offering a data-driven approach to personalized mental healthcare. Future research should address ethical concerns, standardize methodologies, and explore underrepresented populations to maximize AI's transformative potential in mental health.

Using AI-Based Technologies to Help Nurses Detect Behavioral Disorders: Narrative Literature Review

BACKGROUND

The behavioral and psychological symptoms of dementia (BPSD) are common among people with dementia and have multiple negative consequences. Artificial intelligence-based technologies (AITs) have the potential to help nurses in the early prodromal detection of BPSD. Despite significant recent interest in the topic and the increasing number of available appropriate devices, little information is available on using AITs to help nurses striving to detect BPSD early.

OBJECTIVE

The aim of this study is to identify the number and characteristics of existing publications on introducing AITs to support nursing interventions to detect and manage BPSD early.

METHODS

A literature review of publications in the PubMed database referring to AITs and dementia was conducted in September 2023. A detailed analysis sought to identify the characteristics of these publications. The results were reported using a narrative approach.

RESULTS

A total of 25 publications from 14 countries were identified, with most describing prospective observational studies. We identified three categories of publications on using AITs and they are (1) predicting behaviors and the stages and progression of dementia, (2) screening and assessing clinical symptoms, and (3) managing dementia and BPSD. Most of the publications referred to managing dementia and BPSD.

CONCLUSIONS

Despite growing interest, most AITs currently in use are designed to support psychosocial approaches to treating and caring for existing clinical signs of BPSD. AITs thus remain undertested and underused for the early and real-time detection of BPSD. They could, nevertheless, provide nurses with accurate, reliable systems for assessing, monitoring, planning, and supporting safe therapeutic interventions.