Search Results (6,735)

Background/Objectives: Pain, anxiety, and distress are common yet frequently insufficiently managed issues for patients undergoing radiology and radiotherapy procedures. Immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), are emerging as innovative non-pharmacological approaches to alleviate such burdens through engaging interventions. This review, combining scoping and narrative methodologies, seeks to examine the current application, efficacy, and integration of these technologies to enhance patient care and wellbeing within diagnostic and oncological environments. Methods: Employing a mixed scoping and narrative review approach, this study conducted a systematic search of PubMed, EMBASE, Scopus, and Web of Science databases (no date restrictions—search included studies up to May 2025) to identify relevant studies utilizing VR, AR, MR, or XR for mitigating pain, anxiety, or distress in patients undergoing radiology or radiotherapy. Two independent reviewers selected eligible papers, with data extracted systematically. The narrative analysis supplemented the scoping review by providing contextual insights into clinical relevance and technological challenges. Results: The screening process identified 76 articles, of which 27 were assessed for eligibility and 14 met the inclusion criteria. Most studies focused on oncology and primarily employed VR as the immersive technology. VR has shown promising effects in reducing anxiety and pain—particularly during radiotherapy sessions and invasive procedures—and in supporting patient education through engaging, immersive experiences, making it a valuable approach meriting further investigation. Patient acceptance was notably high, especially among those with elevated distress levels. However, findings in radiology were less consistent, likely due to shorter procedure durations limiting the effectiveness of VR. The variability in outcomes highlights the importance of tailoring immersive interventions to specific procedures and patient needs. The narrative component identified key barriers, such as regulatory hurdles, standardization issues, and implementation challenges, that need addressing for broader clinical adoption. Conclusions: Immersive digital therapeutics are evolving from preliminary research tools toward more structured incorporation into clinical practice. Their future success relies on harmonizing technological advancements with patient-focused design and robust clinical evidence. Achieving this will require collaborative efforts among researchers, industry stakeholders, and healthcare providers. The integration of scoping and narrative review methods in this study offers a comprehensive perspective on the current landscape and informs strategic directions for advancing immersive technologies in radiology and radiotherapy. Full article

Background/Objectives: Standard dental practice is being reshaped by digital technologies, and artificial intelligence (AI) is emerging as one of the most challenging recent innovations. Methods: The present study assessed the interest of Romanian dentists in the integration of AI into their current practice through an anonymous questionnaire distributed to 200 respondents. The questionnaire addressed the integration of AI in dentistry by analyzing the following areas of intervention: stages of patient care, perceived impact on the doctor–patient relationship, data security, implementation costs, and the legislative framework. Results: The results showed that 53.6% of dentists reported low difficulties, 37.3% reported moderate difficulties, and 9.1% reported high difficulties with using digital tools. Dentists’ reported willingness to adopt AI-based solutions was as follows: 58.6% were very willing, 30% were moderately willing, and only 11.4% were not very willing. Currently, 80.5% already use digital techniques in their daily practice. The participants emphasized the need to maintain a strong doctor–patient relationship while recognizing the benefits of increased efficiency. They were aware of the risk of diminishing human connection and trust. Also, data security and the financial stress associated with implementing and maintaining new systems were seen as major obstacles. Conclusions: The dentists surveyed showed an increased interest in modern digital technologies, provided that there is a clear legal framework, a strong data protection system, and the preservation of the doctor–patient relationship based on trust and confidentiality that defines the medical profession. Full article

The increasing complexity in hospital waste management requires innovative solutions that integrate sustainability and regulatory compliance. This study proposes an AI-based decision tool to support the circular management of healthcare waste. The approach combines two key elements: (i) the systematic qualitative analysis of international, European, and national regulations, scientific literature, and best practices aimed at identifying strategic actions; (ii) the prioritization of these actions through machine learning, using a Random Forest classifier. We identified 55 actions, grouped into 13 thematic areas, and used them as input variables to assess their impact on regulatory compliance. The variable importance analysis allowed us to classify actions according to their strategic relevance, guiding the structure of the tool and its user interface. Validation, conducted on four simulated case studies, demonstrated the system’s ability to improve compliance monitoring, operational efficiency, and the implementation of circular economy and Zero-Waste strategies. The proposed model represents a scalable and evidence-based solution capable of supporting the ecological transition of healthcare facilities in line with EU directives and the Sustainable Development Goals. Full article

Background: The management of older diabetic patients in the Southern Africa Development Community (SADC) has been described by several authors as poor due to several constraints and lack of a team care approach. This systematic review aimed to investigate the facilitators and barriers to the effective implementation of interprofessional care (IPC) of the elderly with type 2 diabetes mellitus (T2D) in the SADC region. Methods: A comprehensive literature search was conducted using the Population–Concept–Context (PCC) framework in the search for relevant articles. Out of a total of 155 relevant articles, only 8 articles matched the set criteria and were selected for the final review. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used in the review. Results: The identified facilitators include providing decision support to healthcare workers, training of healthcare workers, use of local languages during the training sessions, and use of certified guidelines in the management of not only T2D but also all the other disease conditions. Barriers like ill-equipped patients with limited opportunities for education and counseling, enormous workload due to staff shortages, and loss to follow-up, among others, were equally identified. Conclusions: This systematic review identifies key facilitators and barriers to implementing effective interprofessional care for type 2 diabetes management in the elderly population of the SADC. Understanding these factors can help healthcare professionals optimize their collaborative efforts, ultimately enhancing the quality of care and improving health outcomes for elderly patients with T2D in the region. Full article

Differentiated thyroid cancer (DTC) poses significant management challenges due to the variable risk of recurrence. This study uses a dataset comprising clinical, pathological, and treatment data from 383 patients to develop and validate machine learning models, combined with feature selection algorithms, for predicting differentiated thyroid cancer recurrence. We evaluated models based on a variety of machine learning technologies (light gradient boosting machine, random forest, k-nearest neighbor, logistic regression, stochastic gradient descent, and an emerging deep learner optimized for tabular data: Gandalf) combined with several feature selection methods. Our feature selection technologies include an emerging redundancy-aware wrapper-based feature selection technique, achieving thyroid cancer recurrence prediction accuracy of 94.8 to 95.9% across two validation methods, based only on whether the patient’s tumor’s response was structurally incomplete, whether their tumor’s stage was advanced (III, IVA, or IVB), and the patient’s age. The results underline the potential for machine learning to enhance the precision of recurrence prediction in DTC while developing technologies whose predictive capacity is more easily explained. Using the same dataset, machine learning and feature selection techniques, this study also provides an analysis on predicting American Thyroid Association (ATA) risk scores. The technologies developed as part of this study have potential for improving the personalization of healthcare through the creation of models based on detailed patient-specific clinical attributes. Full article

Background: Telepharmacy, the provision of patient care services by pharmacists through the use of telecommunications technology, is associated with improved diabetes-related outcomes and access to healthcare. The primary aim of this study was to characterize pharmacists’ interventions at a virtual pharmacist-led diabetes clinic (PLDC). The secondary aim was to assess the feasibility of conducting a future cost-effectiveness study of the PLDCs. Methods: This prospective observational feasibility study was conducted within a pharmacist-led clinic at Seha Virtual Hospital, Riyadh, Saudi Arabia. Two intern pharmacists collected data between 31 July 2024 and 31 January 2025. Results: Seventy-five patients (mean [SD] age 50.47 years [14.95]) attended the clinic. The majority were female (58.7%), had type 2 diabetes (86.6%), and were from outside Riyadh (97.3%). The communication with patients was carried out mainly via telephone (73, 97.3%). The mean consultation duration was 7.64 min (SD = 5.68). A total of 179 interventions were conducted, with a mean number of interventions per patient of 2.5 (median 3, min 0, max 5). The most common intervention was patient education and counseling about their disease and medications. While it was feasible to capture the details of pharmacist interventions and resource use data, incomplete data on patient outcomes presented a challenge. Conclusions: Our detailed documentation of pharmacist–patient encounters revealed the ability of pharmacists to identify and manage the problems of diabetes patients at virtual PLDCs. Our feasibility study identified a few challenges that need to be addressed when designing future cost-effectiveness studies. Full article

Background/Objectives: Sepsis is a severe medical condition characterized by a dysregulated host response to infection, with potentially fatal outcomes, requiring early diagnosis and rapid intervention. The limitations of traditional sepsis identification methods, as well as the complexity of clinical data generated in intensive care, have driven increased interest in applying artificial intelligence in this field. The aim of this narrative review article is to analyze how artificial intelligence is being used in the diagnosis and prognosis of sepsis, to present the most relevant current models and algorithms, and to discuss the challenges and opportunities related to integrating these technologies into clinical practice. Methods: We conducted a structured literature search for this narrative review, covering studies published between 2016 and 2024 in databases such as PubMed/Medline, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The review covered models based on machine learning (ML), deep neural networks (DNNs), Recurrent Neural Networks (RNNs), and clinical alert systems implemented in hospitals. The clinical data sources used, algorithms applied, system architectures, and performance outcomes are presented. Results: Numerous artificial intelligence models demonstrated superior performance compared to conventional clinical scores (qSOFA, SIRS), achieving AUC values above 0.90 in predicting sepsis and mortality. Systems such as Targeted Real-Time Early Warning System (TREWS) and InSight have been clinically validated and have significantly reduced the time to treatment initiation. However, challenges remain, such as a lack of model transparency, algorithmic bias, difficulties integrating into clinical workflows, and the absence of external validation in multicenter settings. Conclusions: Artificial intelligence has the potential to transform sepsis management through early diagnosis, risk stratification, and personalized treatment. A responsible, multidisciplinary approach is necessary, including rigorous clinical validation, enhanced interpretability, and training of healthcare personnel to effectively integrate these technologies into everyday practice. Full article

Artificial intelligence (AI) is revolutionarily shaping the entire landscape of medicine and particularly the privileged field of radiology, since it produces a significant amount of data, namely, images. Currently, AI implementation in radiology is continuously increasing, from automating image analysis to enhancing workflow management, and specifically, pediatric neuroradiology is emerging as an expanding frontier. Pediatric neuroradiology presents unique opportunities and challenges since neonates’ and small children’s brains are continuously developing, with age-specific changes in terms of anatomy, physiology, and disease presentation. By enhancing diagnostic accuracy, reducing reporting times, and enabling earlier intervention, AI has the potential to significantly impact clinical practice and patients’ quality of life and outcomes. For instance, AI reduces MRI and CT scanner time by employing advanced deep learning (DL) algorithms to accelerate image acquisition through compressed sensing and undersampling, and to enhance image reconstruction by denoising and super-resolving low-quality datasets, thereby producing diagnostic-quality images with significantly fewer data points and in a shorter timeframe. Furthermore, as healthcare systems become increasingly burdened by rising demands and limited radiology workforce capacity, AI offers a practical solution to support clinical decision-making, particularly in institutions where pediatric neuroradiology is limited. For example, the MELD (Multicenter Epilepsy Lesion Detection) algorithm is specifically designed to help radiologists find focal cortical dysplasias (FCDs), which are a common cause of drug-resistant epilepsy. It works by analyzing a patient’s MRI scan and comparing a wide range of features—such as cortical thickness and folding patterns—to a large database of scans from both healthy individuals and epilepsy patients. By identifying subtle deviations from normal brain anatomy, the MELD graph algorithm can highlight potential lesions that are often missed by the human eye, which is a critical step in identifying patients who could benefit from life-changing epilepsy surgery. On the other hand, the integration of AI into pediatric neuroradiology faces technical and ethical challenges, such as data scarcity and ethical and legal restrictions on pediatric data sharing, that complicate the development of robust and generalizable AI models. Moreover, many radiologists remain sceptical of AI’s interpretability and reliability, and there are also important medico-legal questions around responsibility and liability when AI systems are involved in clinical decision-making. Future promising perspectives to overcome these concerns are represented by federated learning and collaborative research and AI development, which require technological innovation and multidisciplinary collaboration between neuroradiologists, data scientists, ethicists, and pediatricians. The paper aims to address: (1) current applications of AI in pediatric neuroradiology; (2) current challenges and ethical considerations related to AI implementation in pediatric neuroradiology; and (3) future opportunities in the clinical and educational pediatric neuroradiology field. AI in pediatric neuroradiology is not meant to replace neuroradiologists, but to amplify human intellect and extend our capacity to diagnose, prognosticate, and treat with unprecedented precision and speed. Full article

Advancements in the medical field, particularly in cardiovascular diseases, have significantly improved the diagnosis, management, and prevention of life-threatening presentations and comorbidities. Despite this progress, cardiovascular diseases continue to place a substantial burden on healthcare systems, contributing to nearly 32% of all global deaths according to the World Health Organisation. A predominant complication arising from the treatment of cardiovascular diseases is cardiac ischaemia–reperfusion (I/R) injury, which occurs when blood supply is restored to the myocardium following a period of ischaemia, paradoxically resulting in further tissue damage. There are multiple factors involved in complex pathophysiology and complicated clinical outcomes. Although various therapeutic strategies have been explored to mitigate this injury, an optimal solution has yet to be identified. Therapeutic approaches such as pharmacological interventions and molecular therapy have shown promising prospects in this field. Ongoing research aims to address this unresolved issue, which continues to pose significant challenges for both patients and healthcare professionals. This review aims to explore the multitude of underlying mechanisms of ischaemia–reperfusion injury, and identify current knowledge gaps and new emerging therapeutic interventions. Full article

Violence against nurses in the workplace is a worldwide concern. The high prevalence of these events has negative impacts on professionals, including stress, abandonment of the workplace, and post-traumatic stress syndrome. It is a frequent problem for nurses. As awareness of this problem increases, strategies for prevention and management of aggression and violence have evolved. This study aims to identify strategies, both institutional and personal, to address violence against nurses in the workplace. Methods: A scoping review was conducted with the PRISMA approach, using New Rayyan platform and CEMB for the evaluation of methodological quality. We included all research that studied strategies against workplace violence for nurses in hospitals in Spanish or English published between 2019 and 2024. Results: Among the 28 analyzed full-text studies, two central categories emerged with respect to addressing violence against nurses before (prevention), during (mitigation), and after (response) such events: (1) training and nurses’ action strategies and (2) practical implementation tools. Institutional leadership supporting a zero-tolerance culture; training and resources for early identification of risks; and mitigation strategies with strong emphasis on de-escalation of potential violence, building personal resilience, and support from security personnel are among the effective strategies. Conclusions: Strategies for preventing and handling workplace violence are multidimensional. Leadership engagement, addressing gender biases, conflict management training, resilience building, and security can reduce violence against nurses and its sequelae. It is essential to generate practical knowledge that is easy to apply in healthcare settings. More research is needed, especially in Latin America. Full article

The graded management of structured sensitive data has become a key challenge in data security governance, particularly amid digital transformation in sectors such as government, finance, and healthcare. The existing methods suffer from limited generalization, low efficiency, and reliance on static rules. This paper proposes PPM-SACG, a privacy preference matrix-based model for sensitive attribute classification and grading. The model adopts a three-stage architecture: (1) composite sensitivity metrics are derived by integrating information entropy and group privacy preferences; (2) domain knowledge-guided clustering and association rule mining improve classification accuracy; and (3) mutual information-based hierarchical clustering enables dynamic grouping and grading, incorporating high-sensitivity isolation. Experiments using real-world vehicle management data (50 attributes, 3000 records) and user privacy surveys verify the method’s effectiveness. Compared with existing approaches, PPM-SACG doubles computational efficiency and supports scenario-aware deployment, offering enhanced compliance and practicality for structured data governance. Full article

The increasing adoption of artificial intelligence (AI) in intelligent healthcare systems has elevated the demand for robust medical imaging and vision-based inventory solutions. For an intelligent healthcare inventory system, accurate recognition and classification of medical items, including medicines and emergency supplies, are crucial for ensuring inventory integrity and timely access to life-saving resources. This study presents a hybrid deep learning framework, EfficientDet-BiFormer-ResNet, that integrates three specialized components: EfficientDet’s Bidirectional Feature Pyramid Network (BiFPN) for scalable multi-scale object detection, BiFormer’s bi-level routing attention for context-aware spatial refinement, and ResNet-18 enhanced with triplet loss and Online Hard Negative Mining (OHNM) for fine-grained classification. The model was trained and validated on a custom healthcare inventory dataset comprising over 5000 images collected under diverse lighting, occlusion, and arrangement conditions. Quantitative evaluations demonstrated that the proposed system achieved a mean average precision (mAP@0.5:0.95) of 83.2% and a top-1 classification accuracy of 94.7%, outperforming conventional models such as YOLO, SSD, and Mask R-CNN. The framework excelled in recognizing visually similar, occluded, and small-scale medical items. This work advances real-time medical item detection in healthcare by providing an AI-enabled, clinically relevant vision system for medical inventory management. Full article

The Internet of Things (IoT) is widely used across domains such as smart homes, healthcare, and grids. As billions of devices become connected, strong privacy and security measures are essential to protect sensitive information and prevent cyber-attacks. However, IoT devices often have limited computing power and storage, making it difficult to implement robust security and manage large volumes of data. Existing studies have explored integrating blockchain and Deep Neural Networks (DNNs) to address security, storage, and data dissemination in IoT networks, but they often fail to fully leverage the mutual enhancement between them. This paper proposes DeepChainIoT, a blockchain–DNN integrated framework designed to address centralization, latency, throughput, storage, and privacy challenges in generic IoT networks. It integrates smart contracts with a Long Short-Term Memory (LSTM) autoencoder for anomaly detection and secure transaction encoding, along with an optimized Practical Byzantine Fault Tolerance (PBFT) consensus mechanism featuring transaction prioritization and node rating. On a public pump sensor dataset, our LSTM autoencoder achieved 99.6% accuracy, 100% recall, 97.95% precision, and a 98.97% F1-score, demonstrating balanced performance, along with a 23.9× compression ratio. Overall, DeepChainIoT enhances IoT security, reduces latency, improves throughput, and optimizes storage while opening new directions for research in trustworthy computing. Full article

Background/Objectives: In today’s rapidly evolving healthcare landscape, fostering a culture of innovation and continuous improvement is essential—especially within a nursing association that leads individual and organizational credentialing. Methods: Colleagues from the American Nurses Enterprise (ANE) Innovation Department and the Institute for Nursing Research and Quality Management collaborated to develop the Culture of Innovation and Quality Model^TM. This process involved conducting a literature review, developing a survey instrument, and administering a pilot pre-survey to ANE employees to collect baseline data. Future research will include a comparison with a post-survey after interventions aimed at strengthening the culture of innovation and quality. Results: The results of the pilot pre-survey were high overall and guided the team in identifying areas with the greatest opportunities for improvement. Based on these findings, interventions are being developed that will be implemented at ANE to enhance the practice of and promote the synergy between innovation and quality. Conclusions: Achieving and sustaining high-quality standards of care and advancing the professional development of nurses requires a culture where staff feel safe and have opportunities to create, innovate, improve, and learn. This will help promote an environment where people thrive while ensuring that the nursing profession and practice remain cutting-edge and aligned with emerging technologies and evolving healthcare complexities. The Culture of Innovation and Quality Model^TM may provide a blueprint for organizations who seek to advance innovation and quality knowledge, engagement, and practices and assist their employees in providing better service to colleagues, partners, and customers while adapting to the evolving healthcare environment. Full article

Adherence to medications is a significant challenge in chronic disease management. Poor adherence can lead to adverse patient outcomes including disease progression, increased morbidity, reduced quality of life, higher hospitalization rates, increased medical costs, and mortality. Medical adherence is a complex issue, influenced by multiple factors, including patient-related, medication-related, and healthcare system-related barriers. This review explores reasons for both intentional non-adherence, such as patients underestimating the consequences of the disease, inadequate education or poor healthcare provider–patient communication, and unintentional non-adherence, including forgetfulness, pathophysiological barriers, socioeconomic barriers (including lifestyle and patient factors), or healthcare resource limitations. Multifaceted, patient-tailored interventions that could improve adherence are discussed, including promoting health education, enhancing healthcare provider–patient engagement, and exploring alternative medical solutions and emerging technological advances. No single approach fits all; this review aims to deepen the understanding of intentional and unintentional non-adherence and to inform targeted interventions to empower patients, foster trust, and improve adherence for those with chronic conditions. Full article