Large language models (LLMs), Generative AI (GAI), and Generative Vision Models (GVMs) have become transformative technologies in healthcare, offering new landscapes for diagnostics, patient communication, and research innovation. To map the applications of GAI, LLMs, and GVMs in healthcare, we conducted a systematic literature review guided by the PRISMA. After screening and quality assessment, 96 studies were synthesized to address four research questions: medical imaging applications, patient communication, specialized medical research models, and global healthcare initiatives. Findings indicate that GVMs, such as GANs, DiffMIC, and WSGAN, enhance the accuracy and segmentation of medical imaging, while GAI and LLM-based chatbots, including ChatGPT and IBM Watsonx Assistant, improve patient interaction and administrative efficiency. Specialized models, including Med-PaLM 2, BioGPT, and MedCPT, can be used to perform with high capability in drug discovery, genomic analysis, and evidence-based research. Furthermore, international case studies demonstrate regional adaptations, such as AlphaFold in the UK and scGPT in Canada, which are representative of innovation in public health and personalized care. In general, GAI, LLMs, and GVMs redefine healthcare operations and research worldwide by enabling precision diagnostics, customized communication, and accelerated biomedical innovation.
Background: Diabetic retinopathy is a preventable cause of vision loss, but screening coverage is limited when retinal imaging, expert interpretation, referral completion, and access to treatment are not uniformly available. Objective: The purpose of this review is to assess the clinical evidence, regulatory status, and health-system applicability of three FDA-cleared autonomous diabetic retinopathy screening platforms: LumineticsCore (formerly IDx-DR), EyeArt, and AEYE-DS. Scope: The review will address diagnostic performance, imageability, camera compatibility, acquisition workflow, evidence provenance, geographic validation, referral burden, and deployment context. CE-marked and international non-FDA systems are examined as comparators to provide context for global innovation. Methods: A narrative synthesis of evidence from January 2010 to April 2026 was performed using peer-reviewed studies, regulatory submissions, and real-world reports. Endpoints, reference standards, camera protocols, study populations, and ungradable-image handling differed across sources, precluding quantitative pooling. Key Findings: Autonomous DR screening has progressed from proving technical feasibility to demonstrating pathway fit. LumineticsCore aligns with controlled-workflow autonomy, EyeArt with program-scale triage, and AEYE-DS with portable access expansion. CE-marked and international systems broaden the field through handheld, smartphone-based, offline, and locally adapted models. Clinical value is shaped not only by sensitivity and specificity, but also by imageability, true screening denominator, referral burden, evidence maturity, patient follow-up, and local care capacity. Conclusions: Autonomous screening is most likely to reduce preventable vision loss when it operates as a care pathway linking image capture, referral completion, and treatment initiation. Future evidence should evaluate systems under fair local conditions, measure completed screening and treatment outcomes, and align each platform with the clinical capacity of the setting in which it is deployed
Background Localized scleroderma (LS) is a chronic inflammatory skin disease that can cause long-term functional and cosmetic morbidity, particularly in children. The Localized Scleroderma Cutaneous Assessment Tool (LoSCAT) is the standard for clinical evaluation but remains manual and time-intensive. Automating components of LoSCAT could improve scalability and consistency for clinical monitoring and research. Methods We retrospectively curated the largest number of de-identified clinical photographs from pediatric LS patients enrolled in the National Registry for Childhood Onset Scleroderma during 2010–2025. We developed a privacy-preserving R Shiny platform to support image labeling and quality control using LoSCAT-defined anatomic regions. Six trained raters labeled 5,965 images. For model development, we retained only single-label images and merged the original 18 LoSCAT-defined body regions into 12 side-agnostic classes. After this filtering process, a total of 2,145 high-quality images were included for training and evaluation. A Vision Transformer (ViT) model was fine-tuned using a patient-level stratified group split (training: 1,790 images; test: 355 images) to prevent leakage and preserve class distribution. Results The ViT classifier achieved 0.805 accuracy and 0.776 macro-F1 on the held-out test set. Misclassifications occurred primarily between adjacent or visually similar regions (e.g., abdomen vs lower back; thigh vs leg), consistent with clinically plausible anatomic ambiguity rather than random errors. The trained classifier was subsequently applied to additional images to generate body-region labels at scale. Conclusion Automated body-region recognition in pediatric localized scleroderma photographs is feasible and clinically interpretable. This work provides a reproducible, privacy-preserving workflow that enables large-scale image organization and establishes a foundation for future region-specific lesion scoring. This study focuses on body-region classification as a necessary and foundational step toward automated LoSCAT scoring.
Post-COVID obesity has emerged as a significant public health challenge in Dhaka, driven by a complex interplay of socio-economic, lifestyle, and environmental factors that were exacerbated during the COVID-19 pandemic through increased sedentary behavior, unhealthy dietary patterns, and reduced access to healthcare services. This situation has led to a growing number of individuals being medically classified as overweight and obese, thereby accelerating the prevalence of chronic diseases, increasing pressure on the healthcare system, and hindering public health progress in the city. To address this challenge, the present study introduces a novel analytical framework based on a Relative Error Support Vector Machine (RE-SVM) optimized using the Tasmanian Devil Optimiser (TDO), a recently developed metaheuristic particularly effective f, robust algorithm specifically designed transparency and derive actionable insights, eIn order to maintain transparency and also derive actionable insights, explainable AI (XAI) methodologies are incorporated to offer interpretable models that identify the major contributing factors of obesity within the urban fabric of Dhaka.182 The proposed model was trained and tested on a dataset based on the population of Dhaka city, comprising 2,182 records and 17 original attributes. In the full-feature setting, the optimized RE-SVM model achieved an accuracy of 99.06%, with precision, recall, and F1-score values of 0.9906. However, because obesity-level labels are closely related to BMI, which is directly determined by weight and height, an additional leakage-controlled ablation experiment was conducted by entirely removing Weight (kg) and Height (m) before model training. The revised analysis distinguishes between anthropometric-inclusive classification and non-anthropometric obesity-risk prediction. The most critical factor contributing to obesity in Dhaka is weight, particularly extreme weight ranges (<58.00 kg or >95.70 kg), followed by height and family history of overweight. These findings indicate that the proposed framework can identify predictive patterns in structured obesity-level data. However, because the dataset should not be interpreted as an independently collected population-representative Dhaka cohort, the results should be viewed as methodological evidence rather than direct epidemiological evidence. Locally collected and clinically validated Dhaka data are required before deriving targeted public health interventions from the model outputs.
The rapid advancement of Information Technology has significantly transformed healthcare delivery, influencing both patients and healthcare professionals. Telemedicine and e-Health applications have emerged as essential tools in nursing practice, enabling remote monitoring, chronic disease management, and improved patient engagement. This systematic review examined 32 English-language studies published between 2018 and 2024 in the PubMed and SpringerLink databases, focusing on the design, implementation, and outcomes of digital health systems in nursing. Most applications are designed for widely used devices such as smartphones and tablets. Findings indicate that remote nursing monitoring can enhance patient safety, improve healthcare professionals’ satisfaction with care delivery, and potentially reduce healthcare costs. Key barriers include digital literacy, data privacy concerns, and institutional resistance to technology adoption. Successful implementation requires affordable, secure, and user-friendly solutions tailored to both patients and nursing staff. Despite the challenges, recent years have seen increased efforts to integrate telemedicine programs into clinical practice, highlighting the growing importance of digital health in global nursing. These insights can inform nursing policy, training, and the design of future digital health interventions.
Background The integration of telemedicine within transition of care models has shown to be a promising solution in improving patient outcomes via its association with reduced 30-day hospital readmission rates. Yet little is known about how transition of care models impact hospitals’ Centers for Medicare and Medicaid Services (CMS’) 5-star quality rankings. As such, this study seeks to examine the associations telemedicine-integrated transitional care programs and quality metrics such as CMS transition of care star ratings and 30-day readmission rates for three measures: acute myocardial infarction (AMI), heart failure (HF), and all cause readmissions. Methods Applying a cross-sectional study design, we collected data from the 2022 American Hospital Association Annual Survey, the CMS Hospital Readmission Reduction Program, Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS), and Area Health Resource Files. Mixed effects modeling was employed to examine associations between telehealth post-discharge services, transition care star ratings, and 30-day readmission rates. Results Hospitals with higher transition care star ratings had lower 30-day readmission rate for HF (3-star: −0.89, 4-star: −0.97, 5-star: −0.96; p < 0.001) and for all-case readmission rate (3-star: −0.30, 4-star: −0.40, 5-star: −0.48; p < 0.001). However, for AMI 30-day readmissions, only 4-star transition of care hospitals reported statistically significant reductions (−0.34, p < 0.05). Our findings show that telehealth-integrated post-discharge services was associated with lower 30-day HF readmission rate among 2-star rated hospitals only (−0.32, p = 0.024), no other associations were found. Conclusion Our findings show that hospitals with higher CMS care transition ratings are associated with positive quality outcome improvements in 30-day AMI, HF, and all cause readmissions, however, the association between hospitals with telehealth services and quality outcomes varied based on facility rating and condition type. This suggests that strategies to integrate telehealth into the care transition process should consider care transition quality rankings and organizational factors such as market competition.
Acute lung injury and acute respiratory distress syndrome (ARDS) remain among the most heterogeneous and clinically challenging syndromes in critical care. Although lung-protective ventilation, prone positioning, fluid optimization, neuromuscular blockade, and extracorporeal support have improved outcomes, mortality and long-term morbidity remain substantial. A major reason is that ARDS is increasingly recognized as a biologically and physiologically heterogeneous syndrome rather than a single disease entity.Artificial intelligence (AI) is beginning to address this gap. Beyond outcome prediction, AI is now being applied to early recognition, automated case identification, chest imaging interpretation, lung ultrasound and electrical impedance tomography enhancement, patient–ventilator asynchrony detection, phenotyping, treatment response prediction, and closed-loop support in intensive care. The new global definition of ARDS further broadens the relevance of AI by extending recognition to earlier-stage patients, to those receiving high-flow oxygen, and to resource-limited settings.The principal value of AI in lung injury lies not in replacing clinicians but in integrating multimodal and longitudinal data into a more continuous and computable representation of disease. When coupled with interpretable modelling, multicenter standardized datasets, and prospective validation, AI could help move lung injury care beyond syndrome-based supportive treatment toward a more individualized, dynamic, and computationally informed model of precision medicine.
Objective To assess healthcare professionals’ (HCPs) attitudes and perceptions towards digital health technologies (DHTs) and identify predictors of willingness to adopt DHTs in a tertiary care setting Methods A cross-sectional study was conducted in a tertiary care teaching hospital in Southern India from March 20th to April 20th, 2024, using a validated 14-item questionnaire (kappa > 0.74) to assess knowledge, usage patterns, and attitudes toward DHTs. Healthcare professionals completed a questionnaire covering demographics, DHT usage, and attitudes towards DHTs. Data were analyzed using descriptive statistics and logistic regression to identify the predictors of willingness to adopt DHTs. Results Among151 participants (mean age 28.19 ± 7.01 years, 53.64% female), the majority were doctors (51.65%), followed by allied healthcare professionals (29.8%), nurses (9.93%), and pharmacists (8.61%). Most participants (85.43%) were aware of DHTs, and 43.05% used devices such as smartwatches and mHealth applications for health monitoring. While 77% were willing to adopt DHTs in practice, 64% expressed concerns regarding data privacy and security. Notably, 81% had never received formal training in digital technology or artificial intelligence in healthcare, and 48% were uncertain about patient acceptance of DHTs in healthcare. The significant predictors of willingness to adopt DHTs included knowledge of DHTs (p = 0.003), current use in practice (p = 0.012), perceived user-friendliness (p < 0.001), and years of experience (p = 0.034). Conclusion Despite HCPs’ positive attitudes towards DHTs, significant barriers exist, including privacy concerns, lack of formal training, and uncertainty about implementation. Successful DHT integration requires (1) structured training programs, (2) robust data security frameworks to address privacy concerns, (3) user-friendly interface design, and (4) targeted interventions for mid-career professionals who show lower adoption willingness.
Background: Hearing loss is the largest modifiable risk factor for dementia with high prevalence in memory clinics. Early identification and intervention are crucial to prevent cognitive decline. Pure-tone audiometry is often lacking in many settings. Smartphone-based hearing screening tests present an innovative solution; however, cognitive impairment poses a barrier to their use. This study aims to explore the perceptions, acceptability, and attitudes of older adults with cognitive impairment toward this tool. Methods: Seventeen cognitively impaired patients participated in this cross-sectional study, using a mixed-method approach. Participants used a self-administered smartphone application, including the Hearing Handicap Inventory for the Elderly, pure-tone hearing screening test, and digit-in-noise test. They completed a System Usability Scale (SUS) questionnaire for quantitative analysis, and qualitative data were collected through three focus group interviews. Results: Participants averaged 71.8 years old, with a mean MoCA score of 25.8. The SUS score was 61 ± 24, indicating marginal acceptability. Thematic analysis revealed five main themes: hearing status, past hearing test experiences, experiences with the smartphone test, test performance and engagement, and attitudes toward the smartphone test. User experience was shaped by several interrelated factors, including ease of use, guidance support, aging, and trust in the technology. Conclusions: Smartphone-based hearing tests are a possible workable option for people with mild cognitive impairment, though their usability remains modest and practical challenges persist. The valuable findings of this study will help refine these tools to better meet their specific needs.
Gastric and intestinal cancers are among the deadliest gastrointestinal diseases, necessitating precise organ segmentation for effective detection and treatment planning. Conventional deep-learning models, such as CNN-based U-Net architectures, struggle with long-range dependencies and complex anatomical variations. This study introduces OVT-Net (Optimized Vision Transformer Network), an innovative deep-learning framework integrating Swin Transformer blocks, EfficientNetB7, Adaptive Contextual Attention (ACA) module, Atrous Spatial Pyramid Fusion (ASPF), and Squeeze-and-Excite (SE) Networks. Unlike traditional architectures, OVT-Net employs a hybrid dual-encoder structure, combining EfficientNetB7 for low-level feature extraction and Swin Transformers for global context modeling, addressing intricate anatomical complexities and imaging variabilities of the gastrointestinal tract. The model is trained on 38,496 MRI/CT scans paired with RLE-encoded masks that contain structural and labeling inconsistencies. These inconsistencies are resolved through a comprehensive preprocessing pipeline incorporating path generation, label restructuring, and augmentation to improve generalizability. Experimental results demonstrate superior performance, with a Dice score of 0.9350, an IoU score of 0.9218, a BCE loss of 0.0716, and robust surface distance metrics (HD95 and ASSD), outperforming conventional segmentation methods. To enhance clinical applicability, Explainable AI (XAI) techniques, including Grad-CAM and Grad-CAM++, provide interpretability by highlighting critical regions, improving model transparency in decision-making. Furthermore, OVT-Net is deployed in a Django-based web application, facilitating real-time segmentation and classification with an average accuracy of 97.5%. This research presents OVT-Net as a transformative AI-driven segmentation model, bridging advanced vision transformers with XAI for enhanced medical diagnostics. Its integration into real-world clinical settings paves the way for improved cancer detection and early intervention.
We aimed to systematically review Blockchain affordances in digital health and synthesize a framework and research agenda for personalized health-care. Following PRISMA guidance, we conducted a qualitative thematic synthesis of peer-reviewed studies (January 2020 – October 2025 search window; no meta-analysis). Blockchain technology is an emerging solution that can meet these needs. However, a nuanced application of Blockchain affordances in digital health is necessary to harness its full potential. This paper comprehensively reviews Blockchain affordances in digital health, aiming to identify perceived affordances and explore recent research in the field. We applied Preferred Reporting Items for Systematic Reviews, following the PRISMA guidelines and the lens of affordance theory to analyze about 5300 relevant papers, with 194 selected for deeper analysis. Our analysis identified 14 Blockchain affordances (access control, decentralization, interoperability, security, tamper-resistance, traceability, anonymity, data provenance, identity, immutability, integrity, privacy, transparency, and trust) that are perceived and realized in personalized health-care. Our study also discovered several constraints in Blockchain implementation, such as security and privacy, interoperability, scalability, and infrastructural support, that require further research attention. This study presents Blockchain research in the digital health domain and informs the design and development of computational medicine for personalized health-care.
Parkinson’s disease (PD), a condition of the brain, causes accidental or intractable tendencies including shaking, stiffness, and issues with balance and coordination. In most cases, symptoms start mildly and get worse with time. Patients may have problems speaking and walking as the illness worsens. Additionally, they may exhibit altered mental and behavioral patterns, sleep disorders, sadness, memory difficulty, and exhaustion. In general, it is difficult to forecast sickness. Additionally, more than 25 % of PD diagnoses are incorrect because of the significant similarity between PD symptoms and other neurological symptoms. This prompted us to conduct a comparative literature review of how cutting-edge Machine Learning (ML) implementations are used in these methodologies with their corresponding datasets, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-nearest neighbors (KNN), Random Forest (RF), and Naïve Bayes (NB) classifiers. To increase accuracy, we have used multi-ensemble methods like the XGBoost Classifier and Ensemble (Majority Voting: RF & LSTM) are also used. Our results are contrasted with those from each study. The Static Spiral Test, which is used to identify tremors, performed significantly better in all experiments after applying XGBoost. As a result, it can be deduced that the multi-modal technique is efficient when used in conjunction with the ensemble method Xgboost classification (Extreme gradient boosting) and Ensemble (Majority Voting: RF & LSTM) that it offers a high accuracy of (95 %, and 96 %) in comparison to other classifier models. The approaches’ performance was assessed using a trustworthy dataset from the UCI ML repository.
The COVID-19 pandemic has accelerated the adoption of digital health, highlighting the critical role of digital health competencies in delivering reliable and effective healthcare services. These competencies remain essential post-pandemic to integrate eHealth technologies, telemedicine, and IoT-enabled healthcare solutions into routine clinical practice. This study aimed to develop and validate a tailored digital competence assessment scale (DigiCAS-HPS) for health professions students and to identify differences in digital competence across various demographic and academic groups. A stratified sampling method selected 717 health professions students from four majors in Dong Thap province, Vietnam. Based on previous literature on digital frameworks, pilot testing, and expert review, we generated, refined, and utilized the questionnaire to collect data from the first sample of 366 students to identify the underlying latent factor structure by an exploratory factor analysis. We then applied a confirmatory factor analysis to affirm the structural validity and model-data fit with the second sample of 351 students. The Mann-Whitney and Kruskal Wallis test determined significant differences in digital competence levels among student groups classified by gender, age, field of study, and academic year. The DigiCAS-HPS scale resulted in 16 items and two factors, encompassing Factor 1 (Digital Interaction & Responsibility) and Factor 2 (Digital Content Development and Software Mastery), and demonstrated valid results in model fit, construct validity, and reliability. Personal factors such as older and soon-to-graduate students revealed significant associations with higher proficiency in digital content development and software mastery (p < 0.05). The DigiCAS-HPS scale might be a helpful tool for educators, healthcare institutions, and policymakers to assess and integrate digital competencies into healthcare education. To address the demands of eHealth, telemedicine, and IoT-enabled healthcare systems, the scale supports the development of a digitally proficient healthcare workforce prepared to navigate the dynamic and technology-driven healthcare landscape.
Cyberchondria is defined as an excessive or repeated online health-related information-seeking behavior exacerbated by information overload and quarantine, resulting in amplified health anxiety. A total of 406 Lebanese participants, residing in Lebanon, participated in this cross-sectional study conducted between February and March 2022 to investigate the association between social media use and cyberchondria severity. Participants filled an online questionnaire assessing the severity of cyberchondria (via short Cyberchondria Severity Scale (CSS-12)), fear of COVID-19 (via the Fear of COVID-19 Scale (FCV–19S)), and social media use. The majority of recruited participants were females (76.6 %) with an average age of 30.87 ± 12.68 years. The average time spent on social media per day was 4.19 ± 2.86 h, and the mean scores per item were 2.27 ± 0.73 and 2 ± 0.71 of CSS-12 and Fear of COVID-19, respectively. Social media use for health-related information and considering health-related information from social media, google, and medical websites reliable, were found to be significantly associated with cyberchondria. The developed multiple linear regression model justified 23.3 % of the variation of cyberchondria severity score. Besides, social media use for health-related information (p-value < 0.001), Fear of COVID-19 (p-value < 0.001), and age (p-value = 0.046) were significantly associated with cyberchondria severity. This implies the importance of social media implementation in the health care field in the forms of e-medicine and telehealth.
Healthcare professionals (HCPs) commonly see the potential of health apps for their patients, but in practice do not actively recommend them during consultation. As quality concerns have been identified as a key barrier, a health and wellness app assessment framework and related quality label was previously developed. Yet, even when health apps are of high quality, recommendation behavior may not necessarily follow due to other factors that are yet to be identified and targeted. The main aim of this study was to explore a wide range of HCP behavioral determinants and identify the key determinants of HCP app recommendation behavior. We used the TDF-checklist, which is based on the Theoretical Domains Framework (TDF), an evidence-based framework for the systematic assessment of behavioral determinants of HCP behavior, and adapted it to the study context. 290 Catalan HCPs filled in the survey. For all determinants, room for improvement (deviation from the maximum), relevance (correlation with anticipated behavior), and the potential for change (based on combining room for improvement and relevance) were assessed. A large majority of HCPs indicated they would recommend high-quality apps to their patients. Overall, HCPs were motivated, but more room for improvement was found for capability and opportunity-related domains. Anticipated recommendation behavior correlated strongest with motivational factors like beliefs about consequences and beliefs about capabilities. The potential for change was highest for nature of the behaviors (habit), beliefs about capabilities and knowledge. When implementing the label, efforts should focus on promoting habit formation for recommending high-quality apps, boosting confidence of HCPs, and providing further knowledge regarding health apps.
This study investigates the opportunities and barriers in the implementation of medical equipment within the context of Healthcare 4.0, offering a comprehensive analysis based on the perspectives of 104 industry experts through a sample survey. Using a methodology that combines literature review with descriptive and non-parametric statistical analysis, including the Friedman and Holm-Sidak tests, the study reveals a clear hierarchy of perceived opportunities and challenges. The results indicate that Remote Monitoring, Technology for Medical Precision, and Microelectronics and Innovation in Healthcare are the most valued opportunities. These results reflect a strong trend toward technologies that promote personalized and preventive healthcare. Conversely, Implementation and Maintenance Costs, Poor Infrastructure and Legacy Systems, and challenges in System Integration and Interoperability emerge as the most significant barriers. The statistical analysis demonstrates significant differences in experts’ perceptions, providing valuable insights for the prioritization of investments and implementation strategies in the healthcare sector. This study contributes to the understanding of the factors that drive and inhibit the adoption of advanced healthcare technologies, offering practical implications for healthcare organizations, policymakers, and researchers. The findings have the potential to guide the development of more effective strategies for implementing Healthcare 4.0, promoting a faster and more successful digital transformation in the healthcare sector, with significant benefits for the quality and accessibility of healthcare in society.
Millions of people worldwide suffer greatly from osteoporosis, a chronic bone disease marked by decreased bone mass and structural degradation. Timely intervention and therapy of osteoporosis depend heavily on accurate early osteoporosis prediction. In the proposed method, use a chronic dataset of patient characteristics and risk variables to present a machine learning framework for osteoporosis prediction. Class imbalance is handled by the pipeline by utilizing synthetic minority over-sampling technique (SMOTE) and other data preprocessing techniques including scaling and normalization. Then, the data was split in an 80:20 ratio and seven features were selected by mutual information. Using an ensemble learning technique and also adjusted the hyperparameters of several classification algorithms such as random forest, k-nearest neighbors, support vector machine, XGBoost and logistic regression. XGBoost, the top-performing algorithm has an AUC score of 81.08%, showing excellent classification performance. Furthermore, the interpretability of the model was improved through the utilization of shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) by the XGBoost. This facilitated a more profound comprehension of the fundamental elements propelling the prediction. At last, develops a web interface where patients can know about their own condition by it. According to the work, the suggested framework is a useful tool for osteoporosis early prognosis which could help medical practitioners make treatment decisions.
Introduction Understanding telehealth users’ acceptance is essential for ensuring effective implementation and may lead to successful, higher quality, and safer telehealth programs. Therefore, this study aimed to measure telehealth acceptance in the population of Saudi Arabia and to explore the associations between sociodemographic variables and intention to use telehealth. Materials and methods This study was conducted online from May 1, 2024, to June 30, 2024. Part 1 of the questionnaire collected sociodemographic data. Part 2 employed the Unified Theory of Acceptance and Use of Technology (UTAUT), which includes performance expectancy (PE), effort expectancy (EE), social influence (SF), and facilitating conditions (FC) in addition to the Behavioral Intention (BI) subscale to examine factors influencing telehealth acceptance. The associations between the sociodemographic variables and each construct of the UTAUT and the associations between the sociodemographic variables of participants who agreed for each construct and BI to use telehealth were analyzed using bivariate logistic regression to evaluate predictors. Results A total of 2234 participants completed the survey. 95.7 % of the participants were positive about using telehealth. PE was a significant predictor of the intention to use telehealth (p < 0.01). EE was also a significant predictor of the positive intention to use telehealth (p < 0.01). SI significantly predicted telehealth usage (p < 0.01), as did the FC construct (p < 0.01). Conclusion Telehealth was highly accepted by the population in KSA. User acceptance of telehealth was influenced by their perception of its benefits, ease of use, social pressure, and the availability of facilitating logistics such as a computer and the internet.
Breast cancer is a leading cause of morbidity and mortality among women worldwide, arising from malignant cell transformations in breast tissue. Early detection is paramount as it significantly improves survival rates and reduces the complexity and cost of treatment. Machine learning has revolutionized this field, providing more precise, efficient, and personalized diagnostic methods. Our research aims to develop a robust predictive model for breast cancer classification through rigorous preprocessing, diverse feature selection techniques, and advanced ensemble learning strategies. A central component of our methodology is the employment of a Stacking Classifier integrated with multiple base classifiers, optimized using RandomizedSearchCV to fine-tune hyperparameters. This process enhances the model’s accuracy, reliability, and generalizability. Significantly, our feature selection process involves three methodologies: filter, wrapper, and embedded methods. By applying these techniques, we identify the most critical features that are consistently selected across all methods. These features are then used to train the model, ensuring that our approach focuses on the most relevant data points for breast cancer classification. Utilizing the Wisconsin Breast Cancer Dataset from the UCI repository, which comprises 569 patient records, our model demonstrates exceptional performance. It achieves a perfect accuracy of 100% and an AUC-ROC of 1.00, indicating flawless sensitivity and specificity. The proposed framework was evaluated using two distinct datasets: the Wisconsin Prognostic Breast Cancer (WPBC) dataset and the Wisconsin Original Breast Cancer (WOBC) dataset. This model stands out for its potential to significantly enhance early detection and treatment strategies, marking a significant advance in applying machine learning to improve healthcare outcomes. Additionally, we have developed a user-friendly web app for breast cancer detection using our predictive model.
