Search Results (4,397)

Background and Objectives: Subclinical paroxysmal atrial fibrillation (AF) is often undetected by conventional screening strategies, until complications emerge. Artificial intelligence (AI) applied to sinus rhythm electrocardiograms has emerged as a promising tool to identify individuals with occult AF and to predict the risk of future incident AF. This scoping review synthesizes evidence from original studies evaluating AI models trained on sinus rhythm ECGs for AF detection or AF prediction. Materials and Methods: A comprehensive search of MEDLINE, Embase, Web of Science, Scopus, and IEEE Xplore was conducted to identify peer-reviewed studies from inception to November 2025. Eligible studies included original investigations in which the model input was a sinus rhythm ECG and the outcome was either paroxysmal AF or new-onset AF. Extracted variables included cohort characteristics, ECG acquisition parameters, AI architecture, model predictive performance, AF prediction horizon, clinical outcomes, and validation strategy. Risk of bias was assessed using PROBAST. Results: Nineteen studies met the inclusion criteria. Retrospective datasets ranging from several thousand to over one million ECGs and convolutional or deep neural network AI architectures were used in most studies. AI-ECG models demonstrated high diagnostic accuracy for detecting subclinical AF (ten studies; AUROC 0.75–0.90) and for predicting long-term new-onset AF (six studies; AUROC 0.69–0.85) from a single sinus rhythm ECG. Robust external validation was reported in eleven studies. Combining AI-ECG models with clinical risk factors improved AF predictive performance in several reports. Key limitations across studies included retrospective design, patient selection, limited calibration reporting, and sparse prospective impact data. Conclusions: AI-based analysis of sinus rhythm ECGs can detect occult AF and stratify future AF risk with moderate-to-high accuracy across multiple populations and healthcare systems. However, rigorous prospective trials, evaluating clinical benefit, cost-effectiveness, calibration across demographic groups, and real-world implementation, are required before broad adoption in clinical practice. Full article

Artificial intelligence (AI) is now embedded across many aspects of healthcare, yet most implementations remain fragmented, task-specific, and layered onto legacy workflows. This paper does not review AI applications in healthcare per se; instead, it examines what an AI-first healthcare system would look like, one in which AI functions as a foundational organizing principle of care delivery rather than an adjunct technology. We synthesize evidence across ambulatory, inpatient, diagnostic, post-acute, and population health settings to assess where AI capabilities are sufficiently mature to support system-level integration and where critical gaps remain. Across domains, the literature demonstrates strong performance for narrowly defined tasks such as imaging interpretation, documentation support, predictive surveillance, and remote monitoring. However, evidence for longitudinal orchestration, cross-setting integration, and sustained impact on outcomes, costs, and equity remains limited. Key barriers include data fragmentation, workflow misalignment, algorithmic bias, insufficient governance, and lack of prospective, multi-site evaluations. We argue that advancing toward AI-first healthcare requires shifting evaluation from accuracy-centric metrics to system-level outcomes, emphasizing human-enabled AI, interoperability, continuous learning, and equity-aware design. Using hypertension management and patient journey exemplars, we illustrate how AI-first systems can enable proactive risk stratification, coordinated intervention, and continuous support across the care continuum. We further outline architectural and governance requirements, including cloud-enabled infrastructure, interoperability, operational machine learning practices, and accountability frameworks—necessary to operationalize AI-first care safely and at scale, subject to prospective validation, regulatory oversight, and post-deployment surveillance. This review contributes a system-level framework for understanding AI-first healthcare, identifies priority research and implementation gaps, and offers practical considerations for clinicians, health systems, researchers, and policymakers. By reframing AI as infrastructure rather than isolated tools, the AI-first approach provides a pathway toward more proactive, coordinated, and equitable healthcare delivery while preserving the central role of human judgment and trust. Full article

The growing economic pressures on healthcare systems have heightened the need for effective and sustainable cost management strategies. This study presents a PRISMA-based systematic review of 210 peer-reviewed articles published between 1974 and 2024, retrieved from the Scopus and Web of Science databases. Following a structured selection and screening process, the articles were analysed to identify dominant cost control tools, contextual applications, and methodological trends across diverse health systems. The findings highlight a strong prevalence of Activity-Based Costing (ABC), Diagnosis-Related Groups (DRG), and benchmarking practices, predominantly in public hospital settings. However, significant thematic gaps remain, particularly concerning low-income countries, interdisciplinary integration, and the evaluation of digital technologies for financial optimisation. This review provides a comprehensive thematic synthesis of international research, consolidating knowledge in healthcare cost management and offering evidence-based recommendations to guide future empirical research, policy design, and strategic planning in health finance. Full article

This paper addresses the critical need for efficient energy management in healthcare facilities, where fluctuating energy demands pose challenges to both operational reliability and sustainability objectives. Traditional energy management approaches often fall short in healthcare settings, resulting in inefficiencies and increased operational costs. To address this gap, the paper explores AI-driven methods for demand forecasting and load balancing and proposes an integrated framework combining Long Short-Term Memory (LSTM) networks, a genetic algorithm (GA), and SHAP (Shapley Additive Explanations), specifically tailored for healthcare energy management. While LSTM has been widely applied in time-series forecasting, its use for healthcare energy demand prediction remains relatively underexplored. In this study, LSTM is shown to significantly outperform conventional forecasting models, including ARIMA and Prophet, in capturing complex and non-linear demand patterns. Experimental results demonstrate that the LSTM model achieved a Mean Absolute Error (MAE) of 21.69, a Root Mean Square Error (RMSE) of 29.96, and an R² of approximately 0.98, compared to Prophet (MAE: 59.78, RMSE: 81.22, R² ≈ 0.86) and ARIMA (MAE: 87.73, RMSE: 125.22, R² ≈ 0.66), confirming its superior predictive performance. The genetic algorithm is employed both to support forecasting optimisation and to enhance load balancing strategies, enabling adaptive energy allocation under dynamic operating conditions. Furthermore, SHAP analysis is used to provide interpretable, within-model insights into feature contributions, improving transparency and trust in AI-driven energy decision-making. Overall, the proposed LSTM–GA–SHAP framework improves forecasting accuracy, supports efficient energy utilisation, and contributes to sustainability in healthcare environments. Future work will explore real-time deployment and further integration with reinforcement learning to enable continuous optimisation. Full article

The incorporation of Artificial Intelligence (AI) into medical services in Saudi Arabia offers a substantial opportunity. Despite the increasing integration of AI techniques such as machine learning, natural language processing, and predictive analytics, there persists an issue in the thorough comprehension of their applications, advantages, and issues within the Saudi healthcare framework. This study aims to perform a thorough systematic literature review (SLR) to assess the current status of AI in Saudi healthcare, determine its alignment with Vision 2030, and suggest practical recommendations for future research and policy. In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, 699 studies were initially obtained from electronic databases, with 24 studies selected after the application of established inclusion and exclusion criteria. The results indicated that AI has been effectively utilised in disease prediction, diagnosis, therapy optimisation, patient monitoring, and resource allocation, resulting in notable advancements in diagnostic accuracy, operational efficiency, and patient outcomes. Nonetheless, limitations to adoption, such as ethical issues, legislative complexities, data protection issues, and shortages in worker skills, were also recognised. This review emphasises the necessity for strong ethical frameworks, regulatory control, and capacity-building efforts to guarantee the responsible and fair implementation of AI in healthcare. Recommendations encompass the creation of national AI ethics and governance frameworks, investment in AI education and training initiatives, and the formulation of modular AI solutions to guarantee scalability and cost-effectiveness. This breakthrough enables Saudi Arabia to realise its Vision 2030 objectives, establishing the Kingdom as a global leader in AI-driven healthcare innovation. Full article

Background: Language barriers remain a major obstacle to equitable healthcare access for immigrants and refugees across Europe. Greece, as both a transit and host country, faces persistent challenges in providing linguistically and culturally appropriate care. Methods: This study presents a narrative literature review synthesizing international, European, and Greek evidence on the effects of limited language proficiency, professional interpretation, and intercultural mediation on healthcare access, patient safety, satisfaction, and clinical outcomes. Peer-reviewed studies and selected grey literature were identified through searches of PubMed, Scopus, Web of Science, and CINAHL. Results: The evidence consistently demonstrates that the absence of professional interpretation is associated with substantially higher rates of clinically significant communication errors, longer hospital stays, increased readmissions, and higher healthcare costs. In contrast, the use of trained medical interpreters and intercultural mediators improves comprehension, shared decision-making, patient satisfaction, and clinical outcomes. Comparative European data from Italy, Spain, Germany, and Sweden show that institutionalized interpretation systems outperform Greece’s fragmented, NGO-dependent approach. Greek studies further reveal that limited proficiency in Greek is associated with reduced service utilization, longer waiting times, and lower patient satisfaction. Conclusions: This narrative review highlights the urgent need for Greece to adopt a coordinated, professionally staffed interpretation and intercultural mediation framework. Strengthening linguistic support within the healthcare system is essential for improving patient safety, equity, efficiency, and the integration of migrant and refugee populations. Full article

Dental caries is a multifactorial chronic infectious disease that impacts healthcare costs globally, caused by alterations of the plaque microbiome and proliferation of cariogenic Streptococcus mutans. Treatments targeting S. mutans, such as alternative strategies using probiotics, might be effective in preventing the development of dental caries. In this study, the probiotic formulation of Lactobacillus reuteri SGL01, vitamin C, and acerola was tested against S. mutans DSM20523. Antimicrobial activity was assessed by deferred antagonism and spot-on-lawn assays for L. reuteri SGL01. MIC and MBC of L. reuteri SGL01 cell-free supernatant (CFS), vitamin C, and acerola were determined with the microdilution method. Time–kill assays determined the bactericidal kinetics for each compound. The checkerboard method was used to evaluate the potential synergistic activity of CFS–vitamin C or CFS–acerola at scalar dilutions from 1 to 8X MIC. Lastly, antibiofilm activity was tested for each compound. Antimicrobial activity of L. reuteri SGL01 was first assessed by classic methods. MIC and MBC values differed for one dilution for all compounds, with values of 25% and 50% for CFS, 9.3 mg/mL and 18.7 mg/mL for vitamin C, and 18.7 mg/mL and 37.5 mg/mL for acerola, respectively. Moreover, time–kill assays confirmed the bactericidal activity at different timepoints: 4 h for CFS, 6 h for vitamin C, and 24 h for acerola. The fractional inhibitory concentration index (FICI) showed indifference for all combinations, and for associations tested at 2, 4, and 8XMIC. S. mutans biofilm production was impaired for all components, with stronger activity by vitamin C and acerola at lower concentrations. The probiotic formulation containing L. reuteri SGl01, vitamin C, and acerola extract exerts a bactericidal effect, especially strong for the CFS, as well as antibiofilm activity. Thus, the combination of these three components could be advantageous for their complementary effects, with use as a novel treatment against the development of dental caries by S. mutans. Full article

The discovery and development of new drugs is a lengthy, complex, and costly process, often requiring 10–20 years to progress from initial concept to market approval, with clinical trials representing the most resource-intensive stage. In recent years, Artificial Intelligence (AI) has emerged as a transformative technology capable of reshaping the entire pharmaceutical research and development (R&D) pipeline. The purpose of this narrative review is to examine the role of AI in drug discovery and development, highlighting its contributions, challenges, and future implications for pharmaceutical sciences and global public health. A comprehensive review of the scientific literature was conducted, focusing on published studies, reviews, and reports addressing the application of AI across the stages of drug discovery, preclinical development, clinical trials, and post-marketing surveillance. Key themes were identified, including AI-driven target identification, molecular screening, de novo drug design, predictive toxicity modelling, and clinical monitoring. The reviewed evidence indicates that AI has significantly accelerated drug discovery and development by reducing timeframes, costs, and failure rates. AI-based approaches have enhanced the efficiency of target identification, optimized lead compound selection, improved safety predictions, and supported adaptive clinical trial designs. Collectively, these advances position AI as a catalyst for innovation, particularly in promoting accessible, efficient, and sustainable healthcare solutions. However, substantial challenges remain, including reliance on high-quality and representative biomedical data, limited algorithmic transparency, high implementation costs, regulatory uncertainty, and ethical and legal concerns related to data privacy, bias, and equitable access. In conclusion, AI represents a paradigm shift in pharmaceutical research and drug development, offering unprecedented opportunities to improve efficiency and innovation. Addressing its technical, ethical, and regulatory limitations will be essential to fully realize its potential as a sustainable and globally impactful tool for therapeutic innovation. Full article

Detecting anomalies in time series data is crucial across several domains, including healthcare, finance, and automotive. Large Language Models (LLMs) have recently shown promising results by leveraging robust model pretraining. However, fine-tuning LLMs with several billion parameters requires a large number of training samples and significant training costs. Conversely, LLMs under a zero-shot learning setting require lower overall computational costs, but can fall short in handling complex anomalies. In this paper, we explore the use of lightweight language models for Time Series Anomaly Detection, either zero-shot or via fine-tuning them. Specifically, we leverage lightweight models that were originally designed for time series forecasting, benchmarking them for anomaly detection against both open-source and proprietary LLMs across different datasets. Our experiments demonstrate that lightweight models (<1 Billion parameters) provide a cost-effective solution, as they achieve performance that is competitive and sometimes even superior to that of larger models (>70 Billions). Full article

Streptomyces is a versatile genus widely used in drug production and biotechnological applications. This study aimed to identify and characterize bioactive compounds produced by Streptomyces UP-AC4 and UP-3.2 strains and evaluate their antibacterial and anticancer activities. The strains were identified as Streptomyces californicus and Streptomyces purpurascens via chemotaxonomy, 16S rRNA sequencing, amplified ribosomal DNA restriction analysis, and phylogenetic analysis. Bioactive compounds were extracted using heat treatments at 63 °C for 30 min or 73–110 °C for 10 min. Antibacterial activity against Staphylococcus aureus, Bacillus cereus, and Escherichia coli was assessed by agar disc assay, with MICs of 0.024–0.195 mg/mL and MBCs of 0.098–0.391 mg/mL for the most effective extracts. Anticancer activity against A549, H1299, and Lu99 lung cancer cells was evaluated using the MTT assay, showing IC₅₀ values of 0.23 ± 0.06 to 4.85 ± 0.64 mg/mL, while exhibiting no toxicity to normal fibroblast cells. HPLC analysis indicated that heat-assisted extraction of UP-AC4 at 73 °C for 10 min enriched a dinactin-associated compound as a predominant metabolite with antibiotic and anticancer activities. In conclusion, Streptomyces UP-AC4 and UP-3.2 produce promising low-cost bioactive compounds with strong potential for pharmaceutical and healthcare applications. Full article

Background/Objectives: This study aimed to determine the prevalence of persistent symptoms and the radiological and laboratory evolution at 6 months and 5 years after discharge in patients hospitalized for SARS-CoV-2 pneumonia during the first wave of the pandemic in Spain and to estimate the healthcare impact of their follow-up. Methods: A retrospective longitudinal observational study was conducted at the “Hospital Central de la Defensa”. A total of 200 patients aged >18 years with a diagnosis of SARS-CoV-2 pneumonia were screened. Clinical, radiological, and laboratory data were collected from electronic medical records. Patients with symptoms or radiological abnormalities at discharge underwent in-person evaluations, while the remainder were assessed by telephone. Results: A total of 182 patients met the inclusion and exclusion criteria. Of these, 112 were assessed in the outpatient setting; 60.7% required in-person evaluations, with normal pulmonary auscultation in 93.6%, complete radiological resolution in 85%, and normalized laboratory parameters in almost all cases. At 6 months, 26.5% presented at least one residual symptom, whereas only three patients (4.5%) reported symptoms at 5 years. No risk factors associated with symptom persistence were identified. The estimated cumulative healthcare cost was EUR 21,627.50. Conclusions: Among patients hospitalized for SARS-CoV-2 pneumonia during the first wave of the pandemic, 26.7% and 4.46% presented at least one persistent symptom at 6 months and 5 years after discharge, respectively. Full article

Background: Aging in Western societies poses a growing challenge, placing increasing pressure on healthcare costs. Early identification of infections in elderly nursing home residents is crucial to reduce complications, mortality, and the burden on emergency departments. Methods: We performed a comparative analysis of machine learning models using XGBoost classifiers for infection detection, addressing incomplete daily physiological measurements (Heart Rate, Oxygen Saturation, Body Temperature, and Electrodermal Activity) through strict imputation protocols. We evaluated three model variants—Basic (clinical only), Air Pollution-added, and Social Media-integrated—while incorporating a novel Basal Module to personalize physiological baselines for each resident. Results: Results from the binary model indicate that physiological data provides a necessary baseline for immediate screening. Notably, social media integration emerged as a powerful forecasting tool, extending the predictive horizon to a 6-day lead time with an F1-score of 0.97. Complementarily, air pollution data ensured robust immediate detection (“nowcasting”). In the multiclass scenario, external data resolved the “semantic gap” of vital signs, improving sensitivity for specific infections (e.g., acute respiratory and urinary tract infections) to over 90%. Conclusions: These findings highlight that the strategic integration of environmental and digital signals transforms the system from a reactive monitor into a proactive early warning tool for long-term care facilities. Full article

Background/Objectives: Hypertensive disorders of pregnancy (HDP) remain a significant cause of maternal and perinatal morbidity worldwide. In some healthcare settings, access to angiogenic testing is limited, underscoring the need for affordable biomarkers to guide risk assessment. NT-proBNP, a marker of myocardial wall stress and cardio-renal dysfunction, may offer complementary prognostic value to the angiogenic sFlt-1/PlGF ratio. Methods: In this prospective multicenter observational study, we enrolled 180 pregnant women and categorized them into preeclampsia (PE, n = 95), non-PE HDP (gestational or chronic hypertension, n = 25), and healthy controls (n = 60). NT-proBNP and sFlt-1/PlGF levels were measured at enrollment, after 20 weeks of gestation, predominantly during the second and third trimesters. Associations with proteinuria, uric acid, creatinine, and maternal–fetal complications were examined using multivariable logistic regression adjusted for maternal age, BMI, and gestational age. Discrimination was assessed using receiver operating characteristic (ROC) curve analysis, and the incremental value of NT-proBNP beyond the sFlt-1/PlGF ratio was evaluated using ΔAUC and net reclassification improvement (NRI). Results: Median NT-proBNP levels were significantly higher in PE compared with non-PE HDP and controls (p < 0.01). NT-proBNP ≥200 pg/mL independently predicted maternal–fetal complications (adjusted OR 3.12, 95% CI 1.41–6.90, p = 0.005) and correlated with proteinuria (r = 0.47), creatinine (r = 0.43), and uric acid (r = 0.40) (all p < 0.001). sFlt-1/PlGF alone yielded an AUC of 0.84 (95% CI 0.77–0.89), while NT-proBNP alone demonstrated an AUC of 0.78 (0.71–0.84). Combining both biomarkers improved discrimination (AUC 0.88, 95% CI 0.82–0.92), with a ΔAUC of 0.04 (p = 0.02) and a continuous NRI of 0.21 (p = 0.03). The 200 pg/mL threshold for NT-proBNP achieved 80% sensitivity and 71% specificity (p < 0.001). Conclusions: NT-proBNP provides independent and complementary prognostic value to the sFlt-1/PlGF ratio in predicting maternal–fetal complications in HDP. A practical threshold of 200 pg/mL aids risk assessment, and integrating NT-proBNP into angiogenic models improves prediction. Further multicenter studies are needed to validate multimarker strategies and their cost-effectiveness. Full article

Applications of smart cities, smart buildings, smart agriculture systems, smart grids, and other smart systems benefit from Internet of Things (IoT) protocols, networks, and architecture. Wireless Sensor Networks (WSNs) in smart systems that employ IoT use wireless communication technologies between sensors in the Things layer and the Fog layer hub. Such wireless protocols and networks include WiFi, Bluetooth, and Zigbee, among others. However, the payload formats of these protocols are heterogeneous, and thus, they lack a unified frame format that ensures interoperability. In this paper, a lightweight, interoperable frame format for low-rate, small-size Wireless Sensor Networks (WSNs) in IoT-based systems is designed, implemented, and tested. The practicality of this system is underscored by the development of a gateway that transfers collected data from sensors that use the unified frame to online servers via message queuing and telemetry transport (MQTT) secured with transport layer security (TLS), ensuring interoperability using the JavaScript Object Notation (JSON) format. The proposed frame is tested using market-available technologies such as Bluetooth and Zigbee, and then applied to smart home applications. The smart home scenario is chosen because it encompasses various smart subsystems, such as healthcare monitoring systems, energy monitoring systems, and entertainment systems, among others. The proposed system offers several advantages, including a low-cost architecture, ease of setup, improved interoperability, high flexibility, and a lightweight frame that can be applied to other wireless-based smart systems and applications. Full article

Background/Objectives: Hyperglycemia in hospitalized patients is associated with an increased risk of complications, morbidity, mortality, and healthcare costs, regardless of a prior diagnosis of diabetes. The hospitalist system can improve various outcomes, including length of stay, medical costs, patient satisfaction, and mortality rates. However, the effects of hospitalist care on blood glucose control in hospitalized patients remain unclear. This study aimed to assess the specific effects of hospitalist services on blood glucose control in hospitalized patients, with a focus on hyperglycemia management and patient outcomes. Methods: This retrospective study reviewed the electronic medical records of patients diagnosed with diabetes at Yonsei Severance Hospital in Yongin, between March 2020 and February 2022. It included adults aged ≥20 years who were hospitalized and had undergone blood glucose measurements during hospitalization. Glycemic control was assessed using hemoglobin A1c, and the blood glucose levels were measured four times daily during hospitalization. Variability was quantified using the coefficient of variation and compared between hospitalist-led and traditional specialty care groups, over a 14-day hospitalization period. Results: Despite a higher baseline risk profile, patients receiving hospitalist-led care experienced significantly more stable glycemic variability over time (p = 0.002), suggesting better inpatient glucose management than those receiving traditional specialty care. Conclusions: Hospitalist-led care was associated with more stable glycemic variability over time in hospitalized patients with diabetes, despite a higher baseline burden of comorbidities and poorer glycemic control at admission. Full article