Search Results (1,711)

Pain management in multiple trauma patients presents a complex clinical challenge due to competing priorities such as hemodynamic instability, polypharmacy, coagulopathy, and the urgency of life-saving interventions. In this context, peripheral nerve blocks (PNBs) are increasingly recognized as a valuable asset for their role in managing pain in patients with multiple traumatic injuries. By reducing reliance on systemic opioids, PNBs support effective pain control and facilitate early mobilization, aligning with enhanced recovery principles. This narrative review summarizes current evidence on the use of PNBs in the context of polytrauma, focusing on their analgesic efficacy, integration within multimodal analgesia protocols, and contribution to improved functional outcomes. Despite these advantages, clinical application is limited by specific concerns, including the potential to mask compartment syndrome, the risk of nerve injury or local anesthetic systemic toxicity (LAST), and logistical barriers in acute trauma settings. Emerging directions in the field include the refinement of ultrasound-guided PNB techniques, the expanded use of continuous catheter systems, and the incorporation of fascial plane blocks for anatomically complex or multisite trauma. Parallel efforts are focusing on the development of decision-making algorithms, improved risk stratification tools, and integration into multimodal analgesic pathways. There is also growing emphasis on standardized clinical protocols, simulation-based training, and patient education to enhance safety and consistency in practice. As evidence continues to evolve, the long-term impact of PNBs on functional recovery, quality of life, and healthcare utilization must be further explored. With thoughtful implementation, structured training, and institutional support, PNBs may evolve into a cornerstone of modern trauma analgesia. Full article

This paper aims to leverage artificial intelligence (AI) to assist physicians in utilizing advanced deep learning techniques integrated into developed models within electronic health records (EHRs) in medical information systems (MISes), which have been in use for over 15 years in health centers across the Republic of Serbia. This paper presents a human-centered AI approach that emphasizes physician decision-making supported by AI models. This study presents two developed and implemented deep neural network (DNN) models in the EHR. Both models were based on data that were collected during the COVID-19 outbreak. The models were evaluated using five-fold cross-validation. The convolutional neural network (CNN), based on the pre-trained VGG19 architecture for classifying chest X-ray images, was trained on a publicly available smaller dataset containing 196 entries, and achieved an average classification accuracy of 91.83 ± 2.82%. The DNN model for optimizing patient appointment scheduling was trained on a large dataset (341,569 entries) and a rich feature design extracted from the MIS, which is daily used in Serbia, achieving an average classification accuracy of 77.51 ± 0.70%. Both models have consistent performance and good generalization. The architecture of a realized MIS, incorporating the positioning of developed AI tools that encompass both developed models, is also presented in this study. Full article

Inflammatory Bowel Diseases (IBDs) are complex, multifactorial disorders with no known cure, necessitating lifelong care and often leading to surgical interventions. This ongoing healthcare requirement, coupled with the increased use of biological drugs and rising disease prevalence, significantly increases the financial burden on the healthcare systems. Thus, a number of novel technological approaches have emerged in order to face some of the pivotal questions still associated with IBD. In navigating the intricate landscape of IBD, biosensors act as indispensable allies, bridging the gap between traditional diagnostic methods and the evolving demands of precision medicine. Continuous progress in biosensor technology holds the key to transformative breakthroughs in IBD management, offering more effective and patient-centric healthcare solutions considering the One Health Approach. Here, we will delve into the landscape of biomarkers utilized in the diagnosis, monitoring, and management of IBD. From well-established serological and fecal markers to emerging genetic and epigenetic markers, we will explore the role of these biomarkers in aiding clinical decision-making and predicting treatment response. Additionally, we will discuss the potential of novel biomarkers currently under investigation to further refine disease stratification and personalized therapeutic approaches in IBD. By elucidating the utility of biosensors across the spectrum of IBD care, we aim to highlight their importance as valuable tools in optimizing patient outcomes and reducing healthcare costs. Full article

Sensor selection in IoT-based smart healthcare systems is a complex fuzzy decision-making problem due to the presence of numerous uncertain and interdependent evaluation criteria. Traditional fuzzy multi-criteria decision-making (MCDM) approaches often assume independence among criteria and rely on aggregation operators that impose sharp transitions between preference levels. These assumptions can lead to decision outcomes with insufficient differentiation, limited discriminatory capacity, and potential issues in consistency and sensitivity. To overcome these limitations, this study proposes a novel fuzzy decision-making framework by integrating Quasi-D-Overlap functions into the fuzzy MARCOS (Measurement of Alternatives and Ranking According to Compromise Solution) method. Quasi-D-Overlap functions represent a generalized extension of classical overlap operators, capable of capturing partial overlaps and interdependencies among criteria while preserving essential mathematical properties such as associativity and boundedness. This integration enables a more intuitive, flexible, and semantically rich modeling of real-world fuzzy decision problems. In the context of real-time health monitoring, a case study is conducted using a hybrid edge–cloud architecture, involving sensor tasks such as heartrate monitoring and glucose level estimation. The results demonstrate that the proposed method provides greater stability, enhanced discrimination, and improved responsiveness to weight variations compared to traditional fuzzy MCDM techniques. Furthermore, it effectively supports decision-makers in identifying optimal sensor alternatives by balancing critical factors such as accuracy, energy consumption, latency, and error tolerance. Overall, the study fills a significant methodological gap in fuzzy MCDM literature and introduces a robust fuzzy aggregation strategy that facilitates interpretable, consistent, and reliable decision making in dynamic and uncertain healthcare environments. Full article

Background: Mediastinal staging plays a critical role in guiding treatment decisions for non-small cell lung cancer (NSCLC). While mediastinoscopy has been the gold standard for assessing mediastinal lymph node involvement, endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has emerged as a minimally invasive alternative with comparable diagnostic accuracy. This systematic review evaluates the diagnostic performance, safety, cost-effectiveness, and feasibility of EBUS-TBNA versus mediastinoscopy for mediastinal staging. Methods: A systematic literature review was conducted in accordance with PRISMA guidelines, including searches in Medline, Scopus, EMBASE, and Cochrane databases for studies published from 2010 onwards. A total of 1542 studies were identified, and after removing duplicates and applying eligibility criteria, 100 studies were included for detailed analysis. The extracted data focused on sensitivity, specificity, complications, economic impact, and patient outcomes. Results: EBUS-TBNA demonstrated high sensitivity (85–94%) and specificity (~100%), making it an effective first-line modality for NSCLC staging. Mediastinoscopy remained highly specific (~100%) but exhibited slightly lower sensitivity (86–90%). EBUS-TBNA had a lower complication rate (~2%) and was more cost-effective, while mediastinoscopy provided larger biopsy samples, essential for molecular and histological analyses. The need for general anaesthesia, longer hospital stays, and increased procedural costs make mediastinoscopy less favourable as an initial approach. Combining both techniques in select cases enhanced overall staging accuracy, reducing false negatives and improving diagnostic confidence. Conclusions: EBUS-TBNA has become the preferred first-line mediastinal staging method due to its minimally invasive approach, high diagnostic accuracy, and lower cost. However, mediastinoscopy remains crucial in cases requiring posterior mediastinal node assessment or larger tissue samples. The integration of both techniques in a stepwise diagnostic strategy offers the highest accuracy while minimizing risks and costs. Given the lower hospitalization rates and economic benefits associated with EBUS-TBNA, its widespread adoption may contribute to more efficient resource utilization in healthcare systems. Full article

The COVID-19 pandemic was a profoundly influential global occurrence in recent history, impacting daily life, economics, and healthcare systems for an extended period. The abundance of data has been essential in creating models to simulate and forecast the dissemination of infectious illnesses, aiding governments and health organizations in making educated decisions. This research primarily focuses on designing a control technique that incorporates the five most important inputs that impact the spread of COVID-19 on the Romanian territory. Quantitative analysis and data filtering are two crucial aspects to consider when developing a mathematical model. In this study the transfer function principle was used as the most accurate method for modeling the system, based on its superior fit demonstrated in a previous study. For the control strategy, a PI (Proportional-Integral) controller was designed to meet the requirements of the intended behavior. Finally, it is showed that for such complex models, the chosen control strategy, combined with fine tuning, led to very accurate results. Full article

Background: Cochlear implantation (CI) in pediatric patients with cochlear nerve deficiencies (CND) remains controversial due to a highly variable clinical population, lack of evidence-based guidelines, and mixed research findings. This study assessed current clinical perspectives and practices regarding CI candidacy in children with CND among hearing healthcare professionals in the USA. Methods: An anonymous 19-question online survey was distributed to CI clinicians nationwide. The survey assessed professional background, experience with aplasia and hypoplasia, and perspectives on CI versus auditory brainstem implant (ABI) candidacy, including imaging practices and outcome expectations. Both multiple-choice and open-ended responses were analyzed to identify trends and reasoning. Results: Seventy-two responses were analyzed. Most clinicians supported CI for hypoplasia (60.2%) and, to a lesser extent, for aplasia (41.7%), with audiologists more likely than neurotologists to favor CI. Respondents cited lower risk, accessibility, and the potential for benefit as reasons to attempt CI before ABI. However, many emphasized a case-by-case approach, incorporating imaging, electrophysiological testing, and family counseling. Only 22.2% considered structural factors the best predictors of CI success. Conclusions: Overall, hearing health professionals in the USA tend to favor CI as a first-line option, while acknowledging the limitations of current diagnostic tools and the importance of individualized, multidisciplinary decision-making in CI candidacy for children with CND. Findings reveal a high variability in clinical perspectives on CI implantation for pediatric aplasia and hypoplasia and a lack of clinical consensus, highlighting the need for more standardized assessment and imaging protocols to provide greater consistency across centers and enable the development of evidence-based guidelines. Full article

The modified Rankin Scale (mRS) is a widely used outcome measure for assessing disability in stroke care; however, its administration is often affected by subjectivity and variability, leading to poor inter-rater reliability and inconsistent scoring. Originally designed for hospital discharge evaluations, the mRS has evolved into an outcome tool for disability assessment and clinical decision-making. Inconsistencies persist due to a lack of standardization and cognitive biases during its use. This paper presents design principles for creating a standardized clinical assessment tool (CAT) for the mRS, grounded in human–computer interaction (HCI) and cognitive engineering principles. Design principles were informed in part by an anonymous online survey conducted with clinicians across Canada to gain insights into current administration practices, opinions, and challenges of the mRS. The proposed design principles aim to reduce cognitive load, improve inter-rater reliability, and streamline the administration process of the mRS. By focusing on usability and standardization, the design principles seek to enhance scoring consistency and improve the overall reliability of clinical outcomes in stroke care and research. Developing a standardized CAT for the mRS represents a significant step toward improving the accuracy and consistency of stroke disability assessments. Future work will focus on real-world validation with healthcare stakeholders and exploring self-completed mRS assessments to further refine the tool. Full article

Data annotation serves as a critical foundation for artificial intelligence (AI) and machine learning (ML). Recently, AI agents powered by large language models (LLMs) have emerged as effective solutions to longstanding challenges in data annotation, such as scalability, consistency, cost, and limitations in domain expertise. These agents facilitate intelligent automation and adaptive decision-making, thereby enhancing the efficiency and reliability of annotation workflows across various fields. Despite the growing interest in this area, a systematic understanding of the role and capabilities of AI agents in annotation is still underexplored. This paper seeks to fill that gap by providing a comprehensive review of how LLM-driven agents support advanced reasoning strategies, adaptive learning, and collaborative annotation efforts. We analyze agent architectures, integration patterns within workflows, and evaluation methods, along with real-world applications in sectors such as healthcare, finance, technology, and media. Furthermore, we evaluate current tools and platforms that support agent-based annotation, addressing key challenges such as quality assurance, bias mitigation, transparency, and scalability. Lastly, we outline future research directions, highlighting the importance of federated learning, cross-modal reasoning, and responsible system design to advance the development of next-generation annotation ecosystems. Full article

Background and Objectives: Diabetes is a global public health challenge, with increasing prevalence worldwide. The implementation of artificial intelligence (AI) in the management of this condition offers potential benefits in improving healthcare outcomes. This study primarily investigates the barriers and facilitators perceived by healthcare professionals in the adoption of AI. Secondarily, by analyzing both quantitative and qualitative data collected, it aims to support the potential development of AI-based programs for diabetes management, with particular focus on a possible bottom-up approach. Materials and Methods: A scoping review was conducted following PRISMA-ScR guidelines for reporting and registered in the Open Science Framework (OSF) database. The study selection process was conducted in two phases—title/abstract screening and full-text review—independently by three researchers, with a fourth resolving conflicts. Data were extracted and assessed using Joanna Briggs Institute (JBI) tools. The included studies were synthesized narratively, combining both quantitative and qualitative analyses to ensure methodological rigor and contextual depth. Results: The adoption of AI tools in diabetes management is influenced by several barriers, including perceived unsatisfactory clinical performance, high costs, issues related to data security and decision-making transparency, as well as limited training among healthcare workers. Key facilitators include improved clinical efficiency, ease of use, time-saving, and organizational support, which contribute to broader acceptance of the technology. Conclusions: The active and continuous involvement of healthcare workers represents a valuable opportunity to develop more effective, reliable, and well-integrated AI solutions in clinical practice. Our findings emphasize the importance of a bottom-up approach and highlight how adequate training and organizational support can help overcome existing barriers, promoting sustainable and equitable innovation aligned with public health priorities. Full article

Objectives: Digitalization has revolutionized dentistry, introducing advanced technological tools that improve diagnostic accuracy and access to healthcare. This study aims to examine the effects of integrating digital technologies into the dental field, analyzing the associated benefits and risks, with particular paid attention to the therapeutic relationship and decision-making autonomy. Materials and Methods: A literature search was conducted in PubMed, Scopus, Web of Science, and Cochrane Library, complemented by Google Scholar for non-indexed studies. The selection criteria included peer-reviewed studies published in English between 2014 and 2024, focusing on digital dentistry, artificial intelligence, and medical ethics. This is a narrative review. Elements of PRISMA guidelines were applied to enhance transparency in reporting. Results: The analysis highlighted that although digital technologies and AI offer significant benefits, such as more accurate diagnoses and personalized treatments, there are associated risks, including the loss of empathy in the dentist–patient relationship, the risk of overdiagnosis, and the possibility of bias in the data. Conclusions: The balance between technological innovation and the centrality of the dentist is crucial. A human and ethical approach to digital medicine is essential to ensure that technologies improve patient care without compromising the therapeutic relationship. To preserve the quality of dental care, it is necessary to integrate digital technologies in a way that supports, rather than replaces, the therapeutic relationship. Full article

Vitamin D is the only vitamin that is conditionally essential, as it is synthesized from precursors after UV light exposure, whilst also being obtained from the diet. It has numerous health benefits, with deficiency becoming a major concern globally, such that dietary supplementation has more recently achieved vital importance to maintain satisfactory levels. In recent years, measurements made from blood have, therefore, become critical to determine the status of vitamin D levels in individuals and the larger population. Tests for vitamin D have routinely relied on laboratory analysis with sophisticated equipment, often being slow and costly, whilst rapid immunoassays have suffered from poor specificity and sensitivity. Here, we have evaluated a new rapid immunoassay test on the market (Rapi-D & IgLoo) to quickly and accurately measure vitamin D levels in small capillary blood specimens and compared this to measurements made using the standard laboratory method of liquid chromatography and mass spectrometry. Our results show that vitamin D can be measured very quickly and over a broad range using the new method, as well as correlate relatively well with standard laboratory testing; however, it cannot be fully relied upon currently to accurately diagnose deficiency or sufficiency in individuals. Our statistical and comparative analyses find that the rapid immunoassay with digital quantification significantly overestimates vitamin D levels, leading to diminished diagnosis of vitamin D deficiency. The speed and simplicity of the rapid method will likely provide advantages in various healthcare settings; however, further calibration of this rapid method and testing parameters for improving quantification of vitamin D from capillary blood specimens is required before integration of it into clinical decision-making pathways. Full article

The prevalence of early non-infectious peritoneal dialysis (PD) catheter complications makes performing PD challenging for patients and difficult for the healthcare team to manage. Three common patient scenarios are presented: catheter flow dysfunction, peri-catheter leaks, and catheter-related abdominal pain. Practice recommendations are integrated into each scenario and tailored to clinical presentation, patient need, and resource availability. The importance of including patients in the decision-making process is emphasized, and examples of how contextual factors modify the proposed approach to complications are given. Full article

This study documents Black Wisconsinites’ birthing experiences and their proposed solutions to improve Black birthing people’s trust in healthcare. Between 2019 and 2022, we conducted semi-structured, longitudinal interviews (both individual and focus group interviews) with those enrolled in a local perinatal support group program for Black birthing people (N = 25), asking about their pregnancy, birthing, and postpartum experiences and their ideas for building trust in healthcare. Using the Daughtering Method and Braun and Clarke’s method of reflexive thematic analysis, we coded the interview data and then iteratively collated the codes into themes and subthemes. Participants described experiencing medical racism, including healthcare trauma and provider bias, during pregnancy and delivery. They drew connections between those experiences and the distrust they felt toward healthcare providers and the healthcare system. They provided actionable strategies that individual providers and the healthcare system can take to build the trust of Black birthing people: employ more Black providers, listen to Black birthing people, exhibit cultural humility, engage in shared decision-making, build personal connections with patients, and spend more time with patients. This study connects Black birthing people’s experiences of medical racism to feelings of medical distrust and provides community-identified actionable suggestions to build trust and shape how we combat racial disparities in healthcare provision and health outcomes. Full article

This study presents a DIKWP-driven artificial consciousness framework for IoT-enabled smart healthcare, integrating a Data–Information–Knowledge–Wisdom–Purpose (DIKWP) cognitive architecture with a software-defined IoT infrastructure. The proposed system deploys DIKWP agents at edge and cloud nodes to transform raw sensor data into high-level knowledge and purpose-driven actions. This is achieved through a structured DIKWP pipeline—from data acquisition and information processing to knowledge extraction, wisdom inference, and purpose-driven decision-making—that enables semantic reasoning, adaptive goal-driven responses, and privacy-preserving decision-making in healthcare environments. The architecture integrates wearable sensors, edge computing nodes, and cloud services to enable dynamic task orchestration and secure data fusion. For evaluation, a smart healthcare scenario for early anomaly detection (e.g., arrhythmia and fever) was implemented using wearable devices with coordinated edge–cloud analytics. Simulated experiments on synthetic vital sign datasets achieved approximately 98% anomaly detection accuracy and up to 90% reduction in communication overhead compared to cloud-centric solutions. Results also demonstrate enhanced explainability via traceable decisions across DIKWP layers and robust performance under intermittent connectivity. These findings indicate that the DIKWP-driven approach can significantly advance IoT-based healthcare by providing secure, explainable, and adaptive services aligned with clinical objectives and patient-centric care. Full article