Application of Artificial Intelligence on Psychological Interventions and Diagnosis: An Overview

The application of artificial intelligence in health communication development: A scoping review

This scoping review explores the integration of Artificial Intelligence (AI) with communication, behavioral, and social theories to enhance health behavior interventions. A systematic search of articles published through February 2024, following PRISMA guidelines, identified 28 relevant studies from 13,723 screened. These studies, conducted across various countries, addressed health issues such as smoking cessation, musculoskeletal injuries, diabetes, chronic diseases and mental health using AI-driven tools like chatbots and apps. Despite AI's potential, a gap exists in aligning technical advancements with theoretical frameworks. The proposed AI Impact Communications Model (AI-ICM) aims to bridge this gap, offering a road map for future research and practice.

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.

Advancing Personalized Interventions: A Paradigm Shift in Psychological and Health-Related Treatment Strategies

In recent years, the field of psychological and health-related interventions has seen a paradigm shift towards personalized and tailored approaches [...].

Use of Digitalisation and Machine Learning Techniques in Therapeutic Intervention at Early Ages: Supervised and Unsupervised Analysis

Advances in technology and artificial intelligence (smart healthcare) open up a range of possibilities for precision intervention in the field of health sciences. The objectives of this study were to analyse the functionality of using supervised (prediction and classification) and unsupervised (clustering) machine learning techniques to analyse results related to the development of functional skills in patients at developmental ages of 0-6 years. We worked with a sample of 113 patients, of whom 49 were cared for in a specific centre for people with motor impairments (Group 1) and 64 were cared for in a specific early care programme for patients with different impairments (Group 2). The results indicated that in Group 1, chronological age predicted the development of functional skills at 85% and in Group 2 at 65%. The classification variable detected was functional development in the upper extremities. Two clusters were detected within each group that allowed us to determine the patterns of functional development in each patient with respect to functional skills. The use of smart healthcare resources has a promising future in the field of early care. However, data recording in web applications needs to be planned, and the automation of results through machine learning techniques is required.

Overcoming Low Adherence to Chronic Medications by Improving their Effectiveness using a Personalized Second-generation Digital System

INTRODUCTION

Low adherence to chronic treatment regimens is a significant barrier to improving clinical outcomes in patients with chronic diseases. Low adherence is a result of multiple factors.

METHODS

We review the relevant studies on the prevalence of low adherence and present some potential solutions.

RESULTS

This review presents studies on the current measures taken to overcome low adherence, indicating a need for better methods to deal with this problem. The use of first-generation digital systems to improve adherence is mainly based on reminding patients to take their medications, which is one of the reasons they fail to provide a solution for many patients. The establishment of a second-generation artificial intelligence system, which aims to improve the effectiveness of chronic drugs, is described.

CONCLUSION

Improving clinically meaningful outcome measures and disease parameters may increase adherence and improve patients' response to therapy.

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

Background

Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care.

Methods

We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data.

Results

Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health.

Conclusions

Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.

[Recent approaches to the diagnosis and therapy of monopolar depression]

Unipolar depression is one of the most significant biomedical problems, which is associated with its high prevalence, a pronounced negative impact on the level of work capacity of the population, worsening of the course of most somatic and neurological diseases, and suicide risk. This review presents current data on approaches to the diagnosis of monopolar depression, both classical (clinical and psychometric) and using modern technologies. The existing approaches to the therapy of monopolar depression - psychopharmacologic, psychotherapeutic, and non-drug biological approaches - are discussed. The advantages of the selective serotonin reuptake inhibitor sertraline are presented, and its use as a first-line drug is justified.

The Arrival of the Metaverse in Neurorehabilitation: Fact, Fake or Vision?

The metaverse is a new technology thought to provide a deeper, persistent, immersive 3D experience combining multiple different virtual approaches in a full continuum of physical–digital interaction spaces. Different from virtual reality (VR) and augmented reality (AR), the metaverse has a service-oriented solid model with an emphasis on social and content dimensions. It has widely been demonstrated that motor or cognitive deficits can be more effectively treated using VR/AR tools, but there are several issues that limit the real potential of immersive technologies applied to neurological patients. In this scoping review, we propose future research directions for applying technologies extracted from the metaverse in clinical neurorehabilitation. The multisensorial properties of the metaverse will boost the embodied cognition experience, thus influencing the internal body representations as well as learning strategies. Moreover, the immersive social environment shared with other patients will contribute to recovering social and psychoemotional abilities. In addition to the many potential pros, we will also discuss the cons, providing readers with the available information to better understand the complexity and limitations of the metaverse, which could be considered the future of neurorehabilitation.