Harnessing the Power of Mobile Phone Technology: Screening and Identifying Autism Spectrum Disorder With Smartphone Apps DOI Open Access

Kavita S. Reddy,

Amar Taksande, Bibin Kurian

et al.

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 26, 2024

Integrating smartphone applications into screening and identifying autism spectrum disorder (ASD) represents a promising innovative frontier within healthcare. This forward-looking paper examines the current landscape of ASD apps, shedding light on their potential advantages addressing navigating significant challenges. One most compelling aspects these apps lies in to democratize access screening, effectively breaking down geographical barriers. By using widespread availability smartphones, make it possible for individuals, caregivers, healthcare providers engage early from virtually anywhere. accessibility is especially crucial underserved areas or regions with limited specialized services. Moreover, offer degree objectivity that traditional methods may need help match. relying data-driven algorithms machine learning, they can provide more impartial assessment child's behavior, minimizing subjective bias. objectivity, combined ability monitor assess development over time, empowers caregivers valuable insights progress. However, as any technological advancement healthcare, integrating not without its share ethical privacy considerations. Ensuring informed consent obtained, when collecting data children, complex critical. Striking right balance between necessary protecting an individual's requires careful thought transparent communication. Additionally, "digital divide" challenge needs be acknowledged addressed. Not all individuals families have equal smartphones literacy required use effectively. disparity must considered developing implementing app-based solutions prevent exacerbating existing inequalities. Nevertheless, future holds promise. Advancements technology, including advanced sensors, wearables, augmented reality, further enhance accuracy depth screening. Interdisciplinary collaboration researchers, developers, clinicians, educators ensure are effective, culturally sensitive, user-friendly. Furthermore, broader systems, electronic health records telehealth platforms, streamline process enable seamless transition diagnosis intervention.

Language: Английский

The Role of Wearables & Technology in Mental Health: Review DOI
Dana El Masri,

Lina Jaber,

Rasha Mashal

et al.

Published: Feb. 26, 2024

The increasing prevalence of mental health disorders worldwide calls for innovative treatment approaches. This scholarly article delves into the rapidly expanding domain wearable technology and digital interventions (DMHIs) as potential countermeasures to obstacles presented by current state care. study scrutinizes incorporation cutting-edge technologies such virtual reality (VR) augmented (AR) services, a move catalyzed significantly COVID-19 pandemic's influence on integration across life's various facets. Through an exhaustive narrative synthesis extant literature, this offers thorough examination efficacy, challenges, future outlook in bolstering support. It investigates pivotal themes including effects these accessibility, user engagement, privacy issues, ethical concerns. Moreover, review probes technologies' capacity mitigate professional shortage enhance care accessibility. evidence indicates that although DMHIs present promising opportunities transforming care, they also introduce distinct challenges demand meticulous consideration strategic deployment. manuscript contributes ongoing dialogue within cyberpsychology, furnishing insights suggestions forthcoming research application technological services.

Language: Английский

Citations

4
An Overview of Tools and Technologies for Anxiety and Depression Management Using AI DOI Open Access

Adrianos Pavlopoulos,

Theodoros Rachiotis, Ilias Maglogiannis

et al.

Published: Aug. 13, 2024

This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms anxiety depression. The primary objectives are identify current AI tools, analyze their practicality efficacy, assess potential benefits risks. A comprehensive literature review was conducted using databases such as ScienceDirect, Google Scholar, PubMed, ResearchGate, focusing on publications from last five years. search utilized keywords including "artificial intelligence," "applications," "mental health," "anxiety," "LLMs" "depression". Various chatbots, mobile applications, wearables, virtual reality settings, large language models (LLMs), were examined categorized based functions mental health care. findings indicate that LLMs, show significant promise symptom management, offering accessible personalized interventions can complement traditional treatments. Tools AI-driven apps, LLMs have demonstrated efficacy reducing depression, improving user engagement outcomes. particular, shown enhancing therapeutic diagnostic treatment plans by providing immediate support resources, thus workload professionals. However, limitations include concerns over data privacy, for over-reliance technology, need human oversight ensure Ethical considerations, security balance between interaction, also addressed. concludes while AI, has significantly aid care, it should be used a to, rather than replacement for, therapists. Future research focus measures, integrating tools with methods, exploring long-term effects health. Further investigation is needed across diverse populations settings.

Language: Английский

Citations

4
An Overview of Tools and Technologies for Anxiety and Depression Management Using AI DOI Creative Commons

Adrianos Pavlopoulos,

Theodoros Rachiotis, Ilias Maglogiannis

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(19), P. 9068 - 9068

Published: Oct. 8, 2024

This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms anxiety depression. The primary objectives are identify current AI tools, analyze their practicality efficacy, assess potential benefits risks. A comprehensive literature review was conducted using databases such as ScienceDirect, Google Scholar, PubMed, ResearchGate, focusing on publications from last five years. search utilized keywords including “artificial intelligence”, “applications”, “mental health”, “anxiety”, “LLMs” “depression”. Various chatbots, mobile applications, wearables, virtual reality settings, large language models (LLMs), were examined categorized based functions mental health care. findings indicate that LLMs, show significant promise symptom management, offering accessible personalized interventions can complement traditional treatments. Tools AI-driven apps, LLMs have demonstrated efficacy reducing depression, improving user engagement outcomes. particular, shown enhancing therapeutic diagnostic treatment plans by providing immediate support resources, thus workload professionals. However, limitations include concerns over data privacy, for overreliance technology, need human oversight ensure Ethical considerations, security balance between interaction, also addressed. concludes while AI, has significantly aid care, it should be used a to, rather than replacement for, therapists. Future research focus measures, integrating tools with methods, exploring long-term effects health. Further investigation is needed across diverse populations settings.

Language: Английский

Citations

4
AI-Enhanced Neurophysiological Assessment DOI
Deepak Kumar, Punet Kumar,

Sushma Pal

et al.

Advances in psychology, mental health, and behavioral studies (APMHBS) book series, Journal Year: 2025, Volume and Issue: unknown, P. 33 - 64

Published: Jan. 3, 2025

Advancements in artificial intelligence (AI) are revolutionizing neurophysiology, enhancing precision and efficiency assessing brain nervous system function. AI-driven neurophysiological assessment integrates machine learning, deep neural networks, advanced data analytics to process complex from electroencephalography, electromyography techniques. This technology enables earlier diagnosis of neurological disorders like epilepsy Alzheimer's by detecting subtle patterns that may be missed human analysis. AI also facilitates real-time monitoring predictive analytics, improving outcomes critical care neurorehabilitation. Challenges include ensuring quality, addressing ethical concerns, overcoming computational limits. The integration into neurophysiology offers a precise, scalable, accessible approach treating disorders. chapter discusses the methodologies, applications, future directions assessment, emphasizing its transformative impact clinical research fields.

Language: Английский

Citations

0
Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models DOI Creative Commons

Tsumugi Isogami,

Nobuyoshi Komuro

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 721 - 721

Published: Jan. 13, 2025

This paper presents a method for estimating arousal and emotional valence levels using non-contact environmental sensing, addressing challenges such as discomfort from long-term device wear privacy concerns associated with facial image analysis. We employed data to develop machine learning models, including Random Forest, Gradient Boosting Decision Trees, the deep model CNN-LSTM, evaluated their accuracy in states. The results indicate that decision tree-based methods, particularly are highly effective states data.

Language: Английский

Citations

0
Wearable physiological monitoring of physical exercise and mental health: A systematic review DOI Creative Commons
Feifei Chen, Lulu Zhao, Lanlan Pang

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Innovations in Digital Therapy and Personalized Support DOI

N. Vinodh,

A.K. Subramani,

M. Vijayalakshmi

et al.

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 153 - 172

Published: March 6, 2025

Transformative role of machine learning in mental health care, with a focus on digital therapy and personalized support. As challenges increase globally, traditional therapeutic approaches face limitations scalability customization. Machine innovations, such as natural language processing (NLP) predictive analytics, offer new avenues for diagnosis, treatment, ongoing care. AI-powered platforms, including chatbots, provide real-time interventions, while support systems analyze user data to tailor strategies. By identifying patterns behaviors symptoms, enhances the effectiveness treatments, promoting timely individualized However, like privacy, algorithmic bias, potential over-reliance technology must be addressed. these technologies evolve, they significantly improve access quality creating scalable responsive diverse populations.

Language: Английский

Citations

0
Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review DOI Creative Commons
Ali Kargarandehkordi, Shizhe Li,

Kaiying Lin

et al.

Biosensors, Journal Year: 2025, Volume and Issue: 15(4), P. 202 - 202

Published: March 21, 2025

The development of digital instruments for mental health monitoring using biosensor data from wearable devices can enable remote, longitudinal, and objective quantitative benchmarks. To survey developments trends in this field, we conducted a systematic review artificial intelligence (AI) models biosensors to predict conditions symptoms. Following PRISMA guidelines, identified 48 studies variety smartphone including heart rate, rate variability (HRV), electrodermal activity/galvanic skin response (EDA/GSR), proxies biosignals such as accelerometry, location, audio, usage metadata. We observed several technical methodological challenges across lack ecological validity, heterogeneity, small sample sizes, battery drainage issues. outline corresponding opportunities advancement the field AI-driven biosensing health.

Language: Английский

Citations

0
A survey on security and privacy issues in wearable health monitoring devices DOI
Bonan Zhang, Chao Chen, Ickjai Lee

et al.

Computers & Security, Journal Year: 2025, Volume and Issue: unknown, P. 104453 - 104453

Published: March 1, 2025

Language: Английский

Citations

0
Intelligent sensing devices and systems for personalized mental health DOI Creative Commons
Yantao Xing, Yang Yang, Kaiyuan Yang

et al.

Med-X, Journal Year: 2025, Volume and Issue: 3(1)

Published: April 2, 2025

Language: Английский

Citations

0