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

Key Issues as Wearable Digital Health Technologies Enter Clinical Care DOI
Geoffrey S. Ginsburg, Rosalind W. Picard, Stephen Friend

et al.

New England Journal of Medicine, Journal Year: 2024, Volume and Issue: 390(12), P. 1118 - 1127

Published: March 20, 2024

The authors address the issues that must be confronted if we are to integrate use of wearable digital health technologies into clinical care in a way provides an enduring benefit patients.

Language: Английский

Citations

55
Recent developments and future perspectives of microfluidics and smart technologies in wearable devices DOI Open Access

Sasikala Apoorva,

Nam‐Trung Nguyen, Kamalalayam Rajan Sreejith

et al.

Lab on a Chip, Journal Year: 2024, Volume and Issue: 24(7), P. 1833 - 1866

Published: Jan. 1, 2024

Wearable devices are increasingly popular in health monitoring, diagnosis, and drug delivery. Advances allow real-time analysis of biofluids like sweat, tears, saliva, wound fluid, urine.

Language: Английский

Citations

22
Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions DOI Creative Commons
Manish Bhaiyya, Debdatta Panigrahi, Prakash Rewatkar

et al.

ACS Sensors, Journal Year: 2024, Volume and Issue: 9(9), P. 4495 - 4519

Published: Aug. 15, 2024

Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration Machine Learning (ML) into biosensors ushered in a new era innovation the field PoCT. This article investigates numerous uses transformational possibilities ML improving for algorithms, which are capable processing interpreting complicated biological data, have transformed accuracy, sensitivity, speed procedures variety healthcare contexts. review explores multifaceted applications models, including classification regression, displaying how they contribute to capabilities biosensors. roles ML-assisted electrochemical sensors, lab-on-a-chip electrochemiluminescence/chemiluminescence colorimetric wearable sensors diagnosis explained detail. Given increasingly important role PoCT, this study serves valuable reference researchers, clinicians, policymakers interested understanding emerging landscape point-of-care diagnostics.

Language: Английский

Citations

21
Application of artificial intelligence in the health management of chronic disease: bibliometric analysis DOI Creative Commons
Minggui Pan, Rong Li, Junfan Wei

et al.

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 11

Published: Jan. 7, 2025

With the rising global burden of chronic diseases, traditional health management models are encountering significant challenges. The integration artificial intelligence (AI) into disease has enhanced patient care efficiency, optimized treatment strategies, and reduced healthcare costs, providing innovative solutions in this field. However, current research remains fragmented lacks systematic, comprehensive analysis. This study conducts a bibliometric analysis AI applications management, aiming to identify trends, highlight key areas, provide valuable insights state Hoping our findings will serve as useful reference for guiding further fostering effective application healthcare. Web Science Core Collection database was utilized source. All relevant publications from inception August 2024 were retrieved. external characteristics summarized using HistCite. Keyword co-occurrences among countries, authors, institutions analyzed with Vosviewer, while CiteSpace employed assess keyword frequencies trends. A total 341 retrieved, originating 775 across 55 published 175 journals by 2,128 authors. notable surge occurred between 2013 2024, accounting 95.31% (325/341) output. United States Journal Medical Internet Research leading contributors Our revealed four primary clusters: diagnosis, care, telemedicine, technology. Recent trends indicate that mobile technologies machine learning have emerged focal points field management. Despite advancements several critical challenges persist. These include improving quality, greater international inter-institutional collaboration, standardizing data-sharing practices, addressing ethical legal concerns. Future should prioritize strengthening partnerships facilitate cross-disciplinary cross-regional knowledge exchange, optimizing more precise ensuring their seamless clinical practice.

Language: Английский

Citations

2
Artificial intelligence-assisted electrochemical sensors for qualitative and semi-quantitative multiplexed analyses DOI Creative Commons
Rocco Cancelliere, Mario Molinara,

Antonio Licheri

et al.

Digital Discovery, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

AI-integrated electrochemical sensors boost peak resolution and sensitivity, enabling precise detection of electroactive species in complex matrices. This method enhances analytical capabilities, providing an analytically robust solution.

Language: Английский

Citations

2
Artificial intelligence in nursing: an integrative review of clinical and operational impacts DOI Creative Commons

Salwa Hassanein,

Rabie Adel El Arab, Amany Abdrbo

et al.

Frontiers in Digital Health, Journal Year: 2025, Volume and Issue: 7

Published: March 7, 2025

Background Advances in digital technologies and artificial intelligence (AI) are reshaping healthcare delivery, with AI increasingly integrated into nursing practice. These innovations promise enhanced diagnostic precision, improved operational workflows, more personalized patient care. However, the direct impact of on clinical outcomes, workflow efficiency, staff well-being requires further elucidation. Methods This integrative review synthesized findings from 18 studies published through November 2024 across diverse settings. Using PRISMA 2020 SPIDER frameworks alongside rigorous quality appraisal tools (MMAT ROBINS-I), examined multifaceted effects integration nursing. Our analysis focused three principal domains: advancements monitoring, efficiency workload management, ethical implications. Results The demonstrates that has yielded substantial benefits. AI-powered monitoring systems, including wearable sensors real-time alert platforms, have enabled nurses to detect subtle physiological changes—such as early fever onset or pain indicators—well before traditional methods, resulting timely interventions reduce complications, shorten hospital stays, lower readmission rates. For example, several reported early-warning algorithms facilitated faster responses, thereby improving safety outcomes. Operationally, AI-based automation routine tasks (e.g., scheduling, administrative documentation, predictive classification) streamlined resource allocation. efficiencies led a measurable reduction nurse burnout job satisfaction, can devote time despite these benefits, challenges remain prominent. Key concerns include data privacy risks, algorithmic bias, potential erosion judgment due overreliance technology. issues underscore need for robust targeted literacy training within curricula. Conclusion holds transformative practice by enhancing both outcomes efficiency. realize benefits fully, it is imperative develop frameworks, incorporate comprehensive education, foster interdisciplinary collaboration. Future longitudinal varied contexts essential validate support sustainable, equitable implementation Policymakers leaders must prioritize investments solutions complement expertise professionals while addressing risks.

Language: Английский

Citations

2
Point-of-care testing: state-of-the art and perspectives DOI Creative Commons
Mario Plebani, James H. Nichols, Peter B. Luppa

et al.

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2024, Volume and Issue: unknown

Published: June 17, 2024

Abstract Point-of-care testing (POCT) is becoming an increasingly popular way to perform laboratory tests closer the patient. This option has several recognized advantages, such as accessibility, portability, speed, convenience, ease of use, ever-growing test panels, lower cumulative healthcare costs when used within appropriate clinical pathways, better patient empowerment and engagement, reduction certain pre-analytical errors, especially those related specimen transportation. On other hand, POCT also poses some limitations risks, namely risk accuracy reliability compared traditional tests, quality control connectivity issues, high dependence on operators (with varying levels expertise or training), challenges data management, higher per individual test, regulatory compliance issues need for validation prior use (especially rapid diagnostic tests; RDTs), well additional preanalytical sources error that may remain undetected in this type testing, which usually based whole blood samples (i.e., presence interfering substances, clotting, hemolysis, etc.). There no doubt a breakthrough innovation medicine, but discussion its requires further debate initiatives. collective opinion paper, composed abstracts lectures presented at two-day expert meeting “Point-Of-Care-Testing: State Art Perspective” (Venice, April 4–5, 2024), aims provide thoughtful overview state-of-the-art POCT, current applications, advantages potential limitations, interesting reflections future perspectives particular field medicine.

Language: Английский

Citations

13
A Comprehensive Review on Advancements in Wearable Technologies: Revolutionizing Cardiovascular Medicine DOI Open Access

Vaishnavi Bhaltadak,

Babaji Ghewade,

Seema Yelne

et al.

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

Published: May 29, 2024

Wearable technologies have emerged as powerful tools in healthcare, offering continuous monitoring and personalized insights outside traditional clinical settings. These devices garnered significant attention cardiovascular medicine for their potential to transform patient care improve outcomes. This comprehensive review provides an overview of wearable technologies' evolution, advancements, applications medicine. We examine the miniaturization sensors, integration artificial intelligence (AI), proliferation remote solutions. Key findings include role wearables early detection conditions, health tracking, management. Challenges such data privacy concerns regulatory hurdles are also addressed. The adoption holds promise shifting healthcare from reactive proactive, enabling precision diagnostics, treatment optimization, preventive strategies. Collaboration among stakeholders is essential harnessing full ushering a new era personalized, proactive healthcare.

Language: Английский

Citations

12
Advances in Wearable Biosensors for Healthcare: Current Trends, Applications, and Future Perspectives DOI Creative Commons
Dang-Khoa Vo, Kieu The Loan Trinh

Biosensors, Journal Year: 2024, Volume and Issue: 14(11), P. 560 - 560

Published: Nov. 18, 2024

Wearable biosensors are a fast-evolving topic at the intersection of healthcare, technology, and personalized medicine. These sensors, which frequently integrated into clothes accessories or directly applied to skin, provide continuous, real-time monitoring physiological biochemical parameters such as heart rate, glucose levels, hydration status. Recent breakthroughs in downsizing, materials science, wireless communication have greatly improved functionality, comfort, accessibility wearable biosensors. This review examines present status biosensor with an emphasis on advances sensor design, fabrication techniques, data analysis algorithms. We analyze diverse applications clinical diagnostics, chronic illness management, fitness tracking, emphasizing their capacity transform health facilitate early disease diagnosis. Additionally, this seeks shed light future healthcare wellness by summarizing existing trends new advancements.

Language: Английский

Citations

12
Smart Solutions for Diet-Related Disease Management: Connected Care, Remote Health Monitoring Systems, and Integrated Insights for Advanced Evaluation DOI Creative Commons

Laura-Ioana Coman,

Marilena Ianculescu,

Elena-Anca Paraschiv

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(6), P. 2351 - 2351

Published: March 11, 2024

The prevalence of diet-related diseases underscores the imperative for innovative management approaches. deployment smart solutions signifies a paradigmatic evolution, capitalising on advanced technologies to enhance precision and efficacy. This paper aims present explore diseases, focusing leveraging technologies, such as connected care, Internet Medical Things (IoMT), remote health monitoring systems (RHMS), address rising diseases. transformative approach is exemplified in case studies tailored RHMS capabilities. showcase potential three introducing novel evaluation method their customisation proactive conditions influenced by dietary habits. RO-SmartAgeing System uniquely addresses age-related aspects, providing an integrated that considers long-term impact choices ageing, marking perspective healthcare. NeuroPredict Platform, complex neuroinformatics, enhances understanding connections between brain health, nutrition, overall well-being, contributing insights healthcare assessments. Focused liver monitoring, HepatoConect system delivers real-time data personalized recommendations, offering distinctive disease management. By integrating cutting-edge these transcend traditional boundaries.

Language: Английский

Citations

10