Healthcare Diagnostics Research Articles

Niobium in electrochemical technologies: advancing sensing and battery applications

Abstract Niobium, a versatile transition metal, plays a vital role in expanding electrochemical technologies due to its unique combination of physical and chemical properties, such as high stability, conductivity, and compatibility with varied materials. This review delves deeply into the applications of niobium in electrochemical sensing and energy storage systems, focusing on its transformational potential. It begins by explaining the fundamental features of niobium that make it an excellent material for these applications. The principles of electrochemical sensors are elaborated, with a focus on their significance in areas such as healthcare diagnostics, environmental monitoring, and industrial processes. The review highlights the fabrication techniques for niobium-based sensors, detailing advancements in sensitivity and specificity achieved through niobium compounds. In the domain of energy storage, the review examines niobium’s integration into lithium-ion, sodium-ion, and lithium-sulfur batteries. It discusses how niobium compounds enhance battery performance, including improvements in energy density, cycling stability, and charge-discharge efficiency. Comparative analyses with conventional materials are presented to underscore the superior functionality of niobium-based systems. By synthesizing current research, the review identifies critical knowledge gaps and potential areas for future investigation, ultimately underscoring niobium’s pivotal role in driving innovation in electrochemical technologies.

Ways to Measure Metals: From ICP-MS to XRF.

This review explores the use of Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and X-ray Fluorescence (XRF) for quantifying metals and metalloids in biological matrices such as hair, nails, blood, bone, and tissue. It provides a comprehensive overview of these methodologies, detailing their technological limitations, application scopes, and practical considerations for selection in both laboratory and field settings. By examining traditional and novel aspects of each method, this review aims to guide researchers and clinical practitioners in choosing the most suitable analytical tool based on their specific needs for sensitivity, precision, speed, and sample preparation. Recent studies highlight enhanced capabilities of both ICP-MS and XRF technologies, making them more adaptable to various analytical needs. ICP-MS is renowned for its unmatched sensitivity and precision in detecting ultra-trace metals and metalloids in complex biological samples, such as lead in plasma or seawater. XRF advancements include lower detection limits and reduced sample preparation time, enabling rapid, non-destructive analyses, ideal for quick field assessments. Portable XRF analyzers have revolutionized on-the-spot testing, providing robust data without traditional wet-lab constraints. Moreover, hybrid techniques combining ICP-MS and XRF features are emerging, offering rapid and precise metal analysis for environmental monitoring, clinical diagnostics, and epidemiological studies. Matching analytical methods to specific research demands is critical. ICP-MS is the gold standard for detailed quantitative analysis in laboratories, while XRF excels in non-destructive, immediate field applications. Selection should consider sample complexity, sensitivity, speed, and cost-efficiency. Integrating ICP-MS and XRF offers a versatile approach to metals analysis, transforming practices in environmental science and healthcare diagnostics. As these technologies evolve, they are promising to expand capabilities in detecting and understanding the roles of metals and metalloids in health and the environment.

Utilization and benefits of point-of-care testing in primary healthcare⁠

Point-of-care testing (POCT) represents a transformative advancement in healthcare, enabling rapid and accurate diagnostics at or near the site of patient care. By reducing reliance on centralized laboratories, POCT expedites clinical decision-making and enhances healthcare delivery, particularly in underserved regions. Its application spans various medical fields, including infectious diseases, chronic disease management, and emergency care. Innovations such as molecular diagnostics, biosensors, and artificial intelligence (AI) integration have improved the precision, reliability, and accessibility of POCT devices, offering significant benefits for patient outcomes and healthcare efficiency. POCT has demonstrated its potential to reduce diagnostic delays, lower hospital admissions, and decrease the burden on overextended healthcare systems. Rapid diagnostics for conditions such as acute myocardial infarction, diabetes, and malaria have resulted in timely interventions, improving survival rates and reducing complications. In addition to clinical benefits, economic analyses highlight the cost-effectiveness of POCT, as it minimizes resource utilization and shortens patient treatment cycles. However, challenges persist in scaling POCT accessibility. Financial barriers, supply chain limitations, regulatory inconsistencies, and workforce training deficits impede its widespread adoption, particularly in low- and middle-income countries. Addressing these issues requires innovative funding models, global regulatory harmonization, and capacity-building initiatives to empower healthcare workers. Technological advancements continue to expand POCT's potential, integrating real-time diagnostics with digital health platforms and epidemiological surveillance systems. These developments promise not only to enhance healthcare delivery but also to improve public health responses to outbreaks and health crises. By bridging the gap between diagnostics and treatment, POCT offers a pathway to more equitable and efficient healthcare systems, transforming how and where care is delivered. Its integration into healthcare frameworks holds the potential to redefine patient care, fostering a future of accessible, high-quality healthcare for all.

Point-of-need diagnostics in a post-Covid world: an opportunity for paper-based microfluidics to serve during syndemics.

Zoonotic outbreaks present with unpredictable threats to human health, food production, biodiversity, national security and disrupt the global economy. The COVID-19 pandemic-caused by zoonotic coronavirus, SARS-CoV2- is the most recent upsurge of an increasing trend in outbreaks for the past 100 years. This year, emergence of avian influenza (H5N1) is a stark reminder of the need for national and international pandemic preparedness. Tools for threat reduction include consistent practices in reporting pandemics, and widespread availability of accurate detection technologies. Wars and extreme climate events redouble the need for fast, adaptable and affordable diagnostics at the point of need. During the recent pandemic, rapid home tests for SARS-CoV-2 proved to be a viable functional model that leverages simplicity. In this perspective, we introduce the concept of syndemnicity in the context of infectious diseases and point-of-need healthcare diagnostics. We also provide a brief state-of-the-art for paper-based microfluidics. We illustrate our arguments with a case study for detecting brucellosis in cows. Finally, we conclude with lessons learned, challenges and opportunities for paper-based microfluidics to serve point-of-need healthcare diagnostics during syndemics.

ADA-NAF: Semi-Supervised Anomaly Detection Based on the Neural Attention Forest

In this study, we present a novel model called ADA-NAF (Anomaly Detection Autoencoder with the Neural Attention Forest) for semi-supervised anomaly detection that uniquely integrates the Neural Attention Forest (NAF) architecture which has been developed to combine a random forest classifier with a neural network computing attention weights to aggregate decision tree predictions. The key idea behind ADA-NAF is the incorporation of NAF into an autoencoder structure, where it implements functions of a compressor as well as a reconstructor of input vectors. Our approach introduces several technical advances. First, a proposed end-to-end training methodology over normal data minimizes the reconstruction errors while learning and optimizing neural attention weights to focus on hidden features. Second, a novel encoding mechanism leverages NAF’s hierarchical structure to capture complex data patterns. Third, an adaptive anomaly scoring framework combines the reconstruction errors with the attention-based feature importance. Through extensive experimentation across diverse datasets, ADA-NAF demonstrates superior performance compared to state-of-the-art methods. The model shows particular strength in handling high-dimensional data and capturing subtle anomalies that traditional methods often do not detect. Our results validate the ADA-NAF’s effectiveness and versatility as a robust solution for real-world anomaly detection challenges with promising applications in cybersecurity, industrial monitoring, and healthcare diagnostics. This work advances the field by introducing a novel architecture that combines the interpretability of attention mechanisms with the powerful feature learning capabilities of autoencoders.

Evaluation of Medical Diagnosis Capabilities of Three Artificial Intelligence Models – ChatGPT-3.5, Google Gemini, Microsoft Copilot: Sustainable Development Goals (SDGs)

Objectives: This study aims to assess and compare the diagnostic accuracy of three artificial intelligence (AI) models—ChatGPT-3.5, Microsoft Copilot, and Google Gemini—through their performance on clinical vignettes. Theoretical Framework: Building on prior research into the application of AI in healthcare, particularly in diagnostic support, this study examines the potential of AI models to aid clinicians by providing accurate medical diagnoses, thus supporting decision-making in clinical contexts. Methodology: A meta-analysis was conducted, followed by a comparative analysis using 34 clinical vignettes from Texas Tech University Health Sciences Center. Each AI model’s responses were evaluated for accuracy in diagnosing medical cases, and statistical significance was tested using the chi-square test. Results and Discussion: ChatGPT-3.5 achieved the highest diagnostic accuracy (70.59%), outperforming Google Gemini (61.76%) and Microsoft Copilot (35.29%). ChatGPT-3.5 provided concise answers, while Google Gemini and Microsoft Copilot included disclaimers and additional recommendations. Chi-square analysis confirmed significant differences in performance, highlighting variations in diagnostic capabilities across models. Research Implications: These findings underscore the importance of model selection when integrating AI into clinical workflows. AI models show promise in diagnostics but vary in approach and accuracy, warranting further refinement. Originality/Value: This study is among the first to compare the diagnostic accuracy of ChatGPT-3.5, Google Gemini, and Microsoft Copilot, contributing valuable insights into AI’s application in healthcare diagnostics and supporting evidence for its potential role in enhancing patient care.

Screen-Printing vs Additive Manufacturing Approaches: Recent Aspects and Trends Involving the Fabrication of Electrochemical Sensors.

A few decades ago, the technological boom revolutionized access to information, ushering in a new era of research possibilities. Electrochemical devices have recently emerged as a key scientific advancement utilizing electrochemistry principles to detect various chemical species. These versatile electrodes find applications in diverse fields, such as healthcare diagnostics and environmental monitoring. Modern designs have given rise to innovative manufacturing protocols, including screen and additive printing methods, for creating sophisticated 2D and 3D electrochemical devices. This perspective provides a comprehensive overview of the screen-printing and additive-printing protocols for constructing electrochemical devices. It is also informed that screen-printed sensors offer cost-effectiveness and ease of fabrication, although they may pose challenges due to the use of toxic volatile inks and limited design flexibility. On the other hand, additive manufacturing, especially the fused filament fabrication (or fused deposition modeling) strategies, allows for intricate three-dimensional sensor designs and rapid prototyping of customized equipment. However, the post-treatment processes and material selection can affect production costs. Despite their unique advantages and limitations, both printing techniques show promise for various applications, driving innovation in the field toward more advanced sensor designs. Finally, these advancements pave the way for improved sensor performance and expand possibilities for academic, environmental, and industrial applications. The future is full of exciting opportunities for state-of-the-art sensor technologies that will further improve our ability to detect and determine various substances in a wide range of environments as researchers continue to explore the many possibilities of electrochemical devices.

Trends in ready-to-use portable electrochemical sensing devices for healthcare diagnosis.

Compared withprevious decades, healthcare has emerged as a key global concern in light of the recurrent outbreak of pandemics. The initial stage in the provision of healthcare involves the process of diagnosis. Countries worldwide advocate for healthcare research due to its efficacy and capacity to assist diverse populations. Enhanced levels of healthcare management can be attained by the implementation of rapid diagnostic procedures and cognitive data analysis. Therefore, there is a constant need for smart therapeutics, analytical tools, and diagnostic systems to improve health and well-being. The past decade witnessed enormous growth in the sensing detection systems integrated into smartphones with printed electrodes and wearable patches for the screening of various healthcare diagnostics biomarkers and therapeutic drugs. This review focuses on the expansion of point-of-care technologies and their incorporation into a broader array of portable devices, a critical aspect in the context of decentralized societies and their healthcare systems. Discussions are broadly focused on the different sensing platforms such as solid electrodes, screen-printed electrodes, and paper-based sensing strategies for the detection of various biomarkers and therapeutic drugs. We also discuss the next-generation healthcare wearable sensing device importance and future research possibilities. Finally, theportable electrochemical sensing devices and their future perspective developments towards healthcare diagnosisare critically summarized.

Lab-on-the-Needles: A Microneedle Patch-Based Mobile Unit for Highly Sensitive Ex Vivo and In Vivo Detection of Protein Biomarkers.

Detection of biomarkers associated with physiological conditions provides critical insights into healthcare and disease management. However, challenges in sampling and analysis complicate the detection and quantification of protein biomarkers within the epidermal layer of the skin and in viscous liquid biopsy samples. Here, we present the "Lab-on-the-Needles" concept, utilizing a microneedle patch-based sensing box (MNP-based SenBox) for mobile healthcare applications. This system facilitates the rapid capture of protein biomarkers directly from the in situ epidermal layer of skin or liquid biopsies, followed by on-needle analysis for immediate assessment. The integration of horseradish peroxidase-incorporated zeolitic imidazolate framework-8 (HRP@ZIF-8) as a sensitive and stable signal probe, the detection limit for anti-SARS-CoV-2 NP IgA antibodies and various SARS-CoV-2 S1P mutant strains improves by at least 1,000-fold compared to FDA-approved commercial saliva lateral flow immune rapid tests. Additionally, the MNP-based SenBox demonstrated minimally invasive monitoring and rapid quantification of inflammatory cytokine levels (TNF-α and IL-1β) in rats within 30 min using a portable ColorReader. This study highlights the potential of the MNP-based SenBox for the minimally invasive collection and analysis of protein biomarkers directly from in situ epidermal layers of skin or liquid biopsies that might facilitate mobile healthcare diagnostics and longitudinal monitoring.

Trends in Aptasensing and the Enhancement of Diagnostic Efficiency and Accuracy.

The field of healthcare diagnostics is navigating complex challenges driven by evolving patient demographics and the rapid advancement of new technologies worldwide. In response to these challenges, these biosensors offer distinctive advantages over traditional diagnostic methods, such as cost-effectiveness, enhanced specificity, and adaptability, making their integration with point-of-care (POC) platforms more feasible. In recent years, aptasensors have significantly evolved in diagnostic capabilities through the integration of emerging technologies such as microfluidics, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) systems, wearable devices, and machine learning (ML), driving progress in precision medicine and global healthcare solutions. Moreover, these advancements not only improve diagnostic accuracy but also hold the potential to revolutionize early detection, reduce healthcare costs, and improve patient outcomes, especially in resource-limited settings. This Account examines key advancements, focusing on how scientific breakthroughs, including artificial intelligence (AI), have improved sensitivity and precision. Additionally, the integration of aptasensors with these technologies has enabled real-time monitoring and data analysis, fostering advances in personalized healthcare. Furthermore, the potential commercialization of aptasensor technologies could increase their availability in clinical settings and support their use as widespread solutions for global health challenges. Hence, this review discusses technological improvements, practical uses, and prospects while also focusing on the challenges surrounding standardization, clinical validation, and interdisciplinary collaboration for widespread application. Finally, ongoing efforts to address these challenges are key to ensure that aptasensors can be effectively implemented in diverse healthcare systems.

EFFECTIVENESS OF HERBAL AND CONVENTIONAL MANAGEMENT INTERVENTIONS OF HEPATITIS B VIRUS.

Liver inflammation caused by the Hepatitis B virus (HBV) could become chronic and unresolved if untreated. Prevention and management of the disease are through vaccination and other conventional interventions. This study evaluated the cost-effectiveness of herbal management (HM) and conventional pharmaceutical management (PM) interventions in treating HBV. A purposive sampling technique was used to administer questionnaires at the University College Hospital (UCH) and Total Healthcare Diagnostic Centre (THDC). Data collection was according to a EuroQol (EQ -5D), while descriptive and inferential analyses were performed. Findings revealed a high prevalence of HBV among females from ages 26 to 50. The majority discovered their health status through free HBV tests. Other than cost-effective interventions, about 73% of cases adopted HM to manage the disease. A significant difference (P<0.00) was observed in the correlation analysis of the current health status of respondents and the intervention management adopted. correlation analysis of the current health status of respondents was significantly different (P<0.01) against the intervention management adopted (P<0.00). The minister of health should emphasize HBV regular screening, subsidized viral load test and free vaccination in both public and private healthcare centres.

Consumer opinion on the use of machine learning in healthcare settings: A qualitative systematic review.

Given the increasing number of artificial intelligence and machine learning (AI/ML) tools in healthcare, we aimed to gain an understanding of consumer perspectives on the use of AI/ML tools for healthcare diagnostics. We conducted a qualitative systematic review, following established standardized methods, of the existing literature indexed in the following databases up to 4 April 2022: OVID MEDLINE, OVID EMBASE, Scopus and Web of Science. Fourteen studies were identified as appropriate for inclusion in the meta-synthesis and systematic review. Most studies (n = 12) were conducted in high-income countries, with data extracted from both mixed methods (42.9%) and qualitative (57.1%) studies. The meta-synthesis identified four overarching themes across the included studies: (1) Trust, fear, and uncertainty; (2) Data privacy and ML governance; (3) Impact on healthcare delivery and access; and (4) Consumers want to be engaged. The current evidence demonstrates consumers' understandings of AI/ML for medical diagnosis are complex. Consumers express a complex combination of both hesitancy and support towards AI/ML in healthcare diagnosis. Importantly, their views of the use of AI/ML in medical diagnosis are influenced by the perceived trustworthiness of their healthcare providers who use these AI/ML tools. Consumers recognize the potential for AI/ML tools to improve diagnostic accuracy, efficiency and access, and express a strong interest to be engaged in the development and implementation process of AI/ML into routine healthcare.

A Convolutional Neural Network (CNN) Based Framework for Enhanced Diagnosis and Classification of COVID-19 Pneumonia

COVID-19 pneumonia is a persistent worldwide health problem that usually affects the most vulnerable groups in society: the newborn and aged populations. Most of the current endeavors toward handling diagnosis and classification of pneumonia have used numerous techniques for machine learning and deep learning, with a particular focus on COVID-19 pneumonia. However, most of these techniques have raised concerns with regard to diagnostic precision as a result of the limited application of advanced algorithms, datasets whose validation is mostly inadequate and predictive capability. To address these limitations, our research introduces a deep learning-based approach by Convolutional Neural Networks (CNNs), which enhances the performance in classifying COVID-19 pneumonia. Salient features of the proposed method include a four-step process: first, data acquisition from a comprehensive chest X-ray dataset on GitHub; second, processing and analyzing the data through visual means like histograms and scatter plots; third, using CNNs supplemented with techniques for data augmentation in supervised learning; lastly, performance evaluation to benchmark against existing models. The present study uses features from X-ray images with the help of CNN's advanced pattern recognition capabilities in pursuit of achieving better generalization and precision in classification. The model achieved an accuracy of 85.70\% and precision of 88.6%, which surpasses the existing techniques and thereby built the promise of improving the accuracy of the diagnostic process, hence, leading to improved care for the patients, and more so forms the foundation on which future AI-powered healthcare diagnostics are expected to take off.

Optimizing Diabetes Prediction: An Evaluation of Machine Learning Models Through Strategic Feature Selection

Diabetes, a widespread chronic ailment in the United States, imposes significant economic and health burdens, impacting quality of life and life expectancy. This study analyzes a clinical dataset of 253,680 patients from the Behavioral Risk Factor Surveillance System (BRFSS). The dataset encompasses 21 predictors, including high blood pressure, cholesterol, body mass index (BMI), smoking, stroke, heart disease, physical activity, fruit consumption, vegetable consumption, alcohol consumption, insurance coverage, lack of medical visits due to financial constraints, general health, days with mental health issues, days with physical injuries in the past 30 days, difficulties in walking, gender, age, income, and education level. The objective is to balance the training dataset, compare different supervised machine learning models, and identify critical clinical features contributing to diabetes using unsupervised feature selection methods. A total of 34 machine learning models in MATLAB2023a were trained and compared. Quadratic Support Vector Machine (SVM), Coarse Gaussian SVM, and Narrow Neural Networks achieved the highest training accuracy (76.3%), while the Bilayered Neural Network attained 74.7% on an unseen test dataset. Among all, Quadratic SVM demonstrated the best overall performance based on average accuracy, precision, recall, and F1 score. Feature selection highlighted nine key predictors: high blood pressure, high cholesterol, BMI, heart disease, physical activity, general health, recent bodily injuries, mobility issues, and age. A model trained on these features achieved a commendable accuracy of 75.4%, demonstrating the feasibility of a simplified, efficient diagnostic tool with a diagnostic efficacy of 0.7. This study underscores the potential of streamlined models to predict diabetes with fewer parameters while maintaining high accuracy, offering a valuable tool for healthcare diagnostics.

PortraitEmotion3D: A Novel Dataset and 3D Emotion Estimation Method for Artistic Portraiture Analysis

Facial Expression Recognition (FER) has been widely explored in realistic settings; however, its application to artistic portraiture presents unique challenges due to the stylistic interpretations of artists and the complex interplay of emotions conveyed by both the artist and the subject. This study addresses these challenges through three key contributions. First, we introduce the PortraitEmotion3D (PE3D) dataset, designed explicitly for FER tasks in artistic portraits. This dataset provides a robust foundation for advancing emotion recognition in visual art. Second, we propose an innovative 3D emotion estimation method that leverages three-dimensional labeling to capture the nuanced emotional spectrum depicted in artistic works. This approach surpasses traditional two-dimensional methods by enabling a more comprehensive understanding of the subtle and layered emotions often in artistic representations. Third, we enhance the feature learning phase by integrating a self-attention module, significantly improving facial feature representation and emotion recognition accuracy in artistic portraits. This advancement addresses this domain’s stylistic variations and complexity, setting a new benchmark for FER in artistic works. Evaluation of the PE3D dataset demonstrates our method’s high accuracy and robustness compared to existing state-of-the-art FER techniques. The integration of our module yields an average accuracy improvement of over 1% in recent FER systems. Additionally, combining our method with ESR-9 achieves a comparable accuracy of 88.3% on the FER+ dataset, demonstrating its generalizability to other FER benchmarks. This research deepens our understanding of emotional expression in art and facilitates potential applications in diverse fields, including human–computer interaction, security, healthcare diagnostics, and the entertainment industry.

Phase control and energy-trapping management for high quality X-ray afterglow imaging

Long-persistent radioluminescence (RL) detectors have recently attracted extensive attention in healthcare diagnostics and nondestructive inspection due to their information storage capabilities. Nevertheless, the preparation and RL mechanisms of flexible ultralong-afterglow scintillators remain unexplored. Herein, we first introduce a dopant-driven phase control strategy to greatly enhance RL intensity and X-ray afterglow duration through suppressing the nonradiative recombination pathway and manipulating electron trap distribution in CsCdCl3: Mn2+/M (M = In3+, Sb3+, Bi3+) perovskite. The photophysical studies reveal the multiple energy transfer channels in multi-exciton emissive cubic (Cub.) CsCdCl3: In3+/Mn2+ and the tunability of trap depth ranging from 0.72 to 0.78 eV. Specially, the X-ray flexible detector based on Cub. CsCdCl3: In3+/Mn2+ achieves a low X-ray detection limit of 45.2 nGyair s−1, a high spatial resolution of 12.5 lp mm−1 and X-ray extension imaging longer than 10 h. These findings provide insight into RL dynamics and optimal storage performance, thereby contributing to motivate future research in X-ray storage detector.

High-Sensitivity, High-Resolution Miniaturized Spectrometers for Ultraviolet to Near-Infrared Using Guided-Mode Resonance Filters.

Miniaturized spectrometers have significantly advanced real-time analytical capabilities in fields such as environmental monitoring, healthcare diagnostics, and industrial quality control by enabling precise on-site spectral analysis. However, achieving high sensitivity and spectral resolution within compact devices remains a significant challenge, particularly when detecting low-concentration analytes or subtle spectral variations critical for chemical and molecular analysis. This study introduces an innovative approach employing guided-mode resonance filters (GMRFs) to address these limitations. Functioning similarly to notch filters, GMRFs selectively block specific spectral bands while allowing others to pass, maximizing energy extraction from incident light and enhancing spectral encoding. Our design incorporates narrow band-stop filters, which are essential for accurate spectrum reconstruction, resulting in improved resolution and sensitivity. Our spectrometer delivers a spectral resolution of 0.8 nm over a range of 370-810 nm. It achieves sensitivity values that are more than ten times greater than those of conventional grating spectrometers during fluorescence spectroscopy of mouse jejunum. This enhanced sensitivity and resolution are particularly beneficial for chemical and biological applications, facilitating the detection of trace analytes in complex matrices. Furthermore, the spectrometer's compatibility with complementary metal oxide semiconductor (CMOS) technology enables scalable and cost-effective production, fostering broader adoption in chemical analysis, materials science, and biomedical research. This study underscores the transformative potential of the GMRF-based spectrometer as an innovative tool for advancing chemical and interdisciplinary analytical applications.

Does access to and use of primary healthcare services influence health insurance uptake? A mixed-methods study in the Wa Municipality, Ghana

ABSTRACT Purpose This study explored access to quality primary healthcare services under Ghana's National Health Insurance Scheme (NHIS), its impact on NHIS enrolment, and the implications for attaining Universal Health Coverage (UHC). Methods A sequential explanatory mixed-method research design was employed using a multistage sampling technique. We collected data from 413 insured individuals and 47 healthcare facilities for quantitative community-level analysis using questionnaires. Purposive sampling was used to select 17 healthcare providers and 20 insured key informants for qualitative investigation using interview guides. Quantitative data were analyzed using descriptive statistics, independent t-tests, and binary logistic regression. Qualitative data were thematically analyzed to explain the quantitative findings. Results The results indicated a positive association between outpatient and inpatient services, maternal healthcare, and laboratory testing. Maternal healthcare and laboratory diagnostics positively influenced the renewal of NHIS membership. Binary logistic regression revealed that access to, and utilization of PHC services significantly increased the likelihood of NHIS membership renewal, influenced by healthcare system factors such as service availability, acceptability, and affordability. Conclusion Our findings suggest that policymakers must enhance the NHIS with improved service delivery to attain UHC in Ghana.

Applications of integrating artificial intelligence and big data: A comprehensive analysis

Abstract The integration of artificial intelligence (AI) and big data technologies has the potential to revolutionize various industries, yet there are complexities and challenges associated with their implementation. This comprehensive study aims to investigate the combined impact of AI and big data on operational efficiency, precision, and security across multiple sectors. By utilizing a methodological analysis of 105 peer-reviewed articles sourced from reputable databases, we systematically explore the diverse applications, key innovations, and transformative potential of these technologies. Our findings uncover significant advancements in healthcare diagnostics, drug discovery, personalized education, and smart farming, highlighting how AI enhances big data analytics to drive notable improvements. Specifically, the study reveals the accuracy of AI in healthcare diagnostics, the efficiency of big data in drug discovery, the personalization of learning experiences through AI in education, and the sustainability advancements in agriculture through smart farming. These results underscore a substantial shift toward more sophisticated data-driven decision-making and operational processes facilitated by the integration of AI and big data. This shift addresses the initial research problem and makes a significant contribution to both academic and practical understanding of the role these technologies play in shaping the future of industry operations. The study concludes that while AI and big data integration offers substantial benefits, addressing associated challenges is crucial for maximizing their impact.

Role and Impact of Method Noise on CT Image Denoising

Background The main emphasis of this study is on the medical Computed Tomography (CT) imaging denoising technique, which plays a major role in interpreting patient illness information for medical diagnosis. CT imaging is indispensable for accurate disease diagnosis, but image quality is affected by noise and other artifacts. The primary objective is to improve the accuracy of denoising algorithms, which consequently increases early disease prediction and enhances the accuracy of diagnostic outcomes. Objective The major objective was to examine the performance of method noise-based Low-dose CT (LDCT) image denoising technique using a Convolutional Neural Network (CNN) in medical imaging. This method aims to suppress noise, improve image quality, and effectively minimize radiation exposure. This method enhances the accuracy of the denoising algorithm, enabling more precise disease diagnosis. Method noise, or residual noise, plays a major role in denoising CT images while preserving fine details and minimizing other artifacts generated during the denoising process. Method noise includes the omitted structural features and other minute artifacts, which are resolved through CNN-based denoising techniques. This approach elevates the overall imaging quality and clarity, resulting in better diagnostic accuracy. Methods The study includes a systematic, method noise-based approach to determine the performance of denoising algorithms in diagnosing various diseases from medical CT images that are often affected by Gaussian noise. It involves selecting a comprehensive dataset, applying a method noise approach using CNN, and evaluating the outcomes through quantitative measures, such as PSNR, SNR, and SSIM. This comparison aims to assess diagnostic interpretation, thereby improving the accuracy and efficacy of the method noise-based technique in CT medical imaging. Results The results illustrate the differential accuracy and performance of CT image denoising techniques when compared to standard filtering methods, as well as after the application of method noise-based denoising techniques. Implementing quantitative measures, such as PSNR, SNR, and SSIM, aims to improve healthcare diagnostics. Conclusion The study concludes that method noise-based CT image denoising algorithms effectively mitigate noise and artifacts while retaining the corners, contours, and precise details of CT images, subsequently improving the efficiency and accuracy of predicting diagnostic results.