Search Results (901)

Aim: Pacifiers play a critical role in the early stages of craniofacial and palate development during infancy. While they provide comfort and aid in soothing, their use can also have significant impacts on the growth and function of the oral cavity. This study aimed to simulate and predict the behavior of six different types of pacifiers and their functional interaction with the tongue and palate, with the goal of understanding their potential effects on orofacial growth and development. Materials and Methods: Biomechanical analysis using Finite Element Analysis (FEA) mathematical models was employed to evaluate the behavior of six different commercial pacifiers in contact with the palate and tongue. Three-dimensional solid models of the palate and tongue were based on the mathematical framework from a 2007 publication. This allowed for a detailed investigation into how various pacifier designs interact with soft and hard oral tissues, particularly the implications on dental and skeletal development. Results: The findings of this study demonstrate that pacifiers exhibit different interactions with the oral cavity depending on their geometry. Anatomical–functional pacifiers, for instance, tend to exert lateral compressions near the palatine vault, which can influence the hard palate and contribute to changes in craniofacial growth. In contrast, other pacifiers apply compressive forces primarily in the anterior region of the palate, particularly in the premaxilla area. Furthermore, the deformation of the tongue varied significantly across different pacifier types: while some pacifiers caused the tongue to flatten, others allowed it to adapt more favorably by assuming a concave shape. These variations highlight the importance of selecting a pacifier that aligns with the natural development of both soft and hard oral tissues. Conclusions: The results of this study underscore the crucial role of pacifier geometry in shaping both the palate and the tongue. These findings suggest that pacifiers have a significant influence not only on facial bone growth but also on the stimulation of oral functions such as suction and feeding. The geometry of the pacifier affects the soft tissues (tongue and muscles) and hard tissues (palate and jaw) differently, which emphasizes the need for careful selection of pacifiers during infancy. Choosing the right pacifier is essential to avoid potential negative effects on craniofacial development and to ensure that the benefits of proper oral function are maintained. Therefore, healthcare professionals and parents should consider these biomechanical factors when introducing pacifiers to newborns. Full article

The population dynamics of high-altitude mountainous areas are shaped by a complex interplay of socioeconomic and environmental drivers. Despite their significance, such regions have received limited scholarly attention. This research identifies and examines the principal determinants of population changes in the high-altitude mountainous zones of Sichuan Province, China. Utilizing a robust quantitative framework, we introduce the Sustainable Population Migration Index (SPMI) to systematically analyze the migration potential over two decades. The findings indicate healthcare accessibility as the most significant determinant influencing resident and rural population changes, while economic factors notably impact urban populations. The SPMI reveals a pronounced deterioration in migration attractiveness, decreasing by 0.27 units on average from 2010 to 2020. Furthermore, a fixed-effects panel regression confirmed the predictive capability of SPMI regarding population trends, emphasizing its value for demographic forecasting. We also develop a Digital Twin-based Simulation and Decision-support Platform (DTSDP) to visualize policy impacts effectively. Scenario simulations suggest that targeted enhancements in healthcare and infrastructure could significantly alleviate demographic pressures. This research contributes critical insights for sustainable regional development strategies and provides an effective tool for informed policymaking. Full article

Background/Objectives: Accurate and accessible perioperative health information empowers patients and enhances recovery outcomes. Artificial intelligence tools, such as ChatGPT, have garnered attention for their potential in health communication. This study evaluates the accuracy and readability of responses generated by ChatGPT to questions commonly asked about breast cancer. Methods: Fifteen simulated patient queries about breast cancer surgery preparation and recovery were prepared. Responses generated by ChatGPT (4o version) were evaluated for accuracy by a pool of breast surgeons using a 4-point Likert scale. Readability was assessed with the Flesch–Kincaid Grade Level (FKGL). Descriptive statistics were used to summarize the findings. Results: Of the 15 responses evaluated, 11 were rated as “accurate and comprehensive”, while 4 out of 15 were deemed “correct but incomplete”. No responses were classified as “partially incorrect” or “completely incorrect”. The median FKGL score was 11.2, indicating a high school reading level. While most responses were technically accurate, the complexity of language exceeded the recommended readability levels for patient-directed materials. Conclusions: The model shows potential as a complementary resource for patient education in breast cancer surgery, but should not replace direct interaction with healthcare providers. Future research should focus on enhancing language models’ ability to generate accessible and patient-friendly content. 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

Background/Objectives: The integration of AI chatbots in nursing education, particularly in simulation-based learning, is advancing rapidly. However, there is a lack of structured evaluation models, especially to assess AI-generated simulations. This article introduces the AI-Integrated Method for Simulation (AIMS) evaluation framework, a dual-phase evaluation framework adapted from the FAITA model, designed to evaluate both prompt design and chatbot performance in the context of nursing education. Methods: This simulation-based study explored the application of an AI chatbot in an emergency planning course. The AIMS framework was developed and applied, consisting of six prompt-level domains (Phase 1) and eight performance criteria (Phase 2). These domains were selected based on current best practices in instructional design, simulation fidelity, and emerging AI evaluation literature. To assess the chatbots educational utility, the study employed a scoring rubric for each phase and incorporated a structured feedback loop to refine both prompt design and chatbox interaction. To demonstrate the framework’s practical application, the researchers configured an AI tool referred to in this study as “Eval-Bot v1”, built using OpenAI’s GPT-4.0, to apply Phase 1 scoring criteria to a real simulation prompt. Insights from this analysis were then used to anticipate Phase 2 performance and identify areas for improvement. Participants (three individuals)—all experienced healthcare educators and advanced practice nurses with expertise in clinical decision-making and simulation-based teaching—reviewed the prompt and Eval-Bot’s score to triangulate findings. Results: Simulated evaluations revealed clear strengths in the prompt alignment with course objectives and its capacity to foster interactive learning. Participants noted that the AI chatbot supported engagement and maintained appropriate pacing, particularly in scenarios involving emergency planning decision-making. However, challenges emerged in areas related to personalization and inclusivity. While the chatbot responded consistently to general queries, it struggled to adapt tone, complexity and content to reflect diverse learner needs or cultural nuances. To support replication and refinement, a sample scoring rubric and simulation prompt template are provided. When evaluated using the Eval-Bot tool, moderate concerns were flagged regarding safety prompts and inclusive language, particularly in how the chatbot navigated sensitive decision points. These gaps were linked to predicted performance issues in Phase 2 domains such as dialog control, equity, and user reassurance. Based on these findings, revised prompt strategies were developed to improve contextual sensitivity, promote inclusivity, and strengthen ethical guidance within chatbot-led simulations. Conclusions: The AIMS evaluation framework provides a practical and replicable approach for evaluating the use of AI chatbots in simulation-based education. By offering structured criteria for both prompt design and chatbot performance, the model supports instructional designers, simulation specialists, and developers in identifying areas of strength and improvement. The findings underscore the importance of intentional design, safety monitoring, and inclusive language when integrating AI into nursing and health education. As AI tools become more embedded in learning environments, this framework offers a thoughtful starting point for ensuring they are applied ethically, effectively, and with learner diversity in mind. Full article

Surgery remains a healthcare intervention with significant risks for patients. Novel technologies can now enhance the peri-operative workflow, with artificial intelligence (AI) and extended reality (XR) to assist with pre-operative planning. This review focuses on innovation in AI, XR and imaging for hepato-biliary surgery planning. The clinical challenges in hepato-biliary surgery arise from heterogeneity of clinical presentations, the need for multiple imaging modalities and highly variable local anatomy. AI-based models have been developed for risk prediction and multi-disciplinary tumor (MDT) board meetings. The future could involve an on-demand and highly accurate AI-powered decision tool for hepato-biliary surgery, assisting the surgeon to make the most informed decision on the treatment plan, conferring the best possible outcome for individual patients. Advances in AI can also be used to automate image interpretation and 3D modelling, enabling fast and accurate 3D reconstructions of patient anatomy. Surgical navigation systems utilizing XR are already in development, showing an early signal towards improved patient outcomes when used for hepato-biliary surgery. Live visualization of hepato-biliary anatomy in the operating theatre is likely to improve operative safety and performance. The technological advances in AI and XR provide new applications in pre-operative planning with potential for patient benefit. Their use in surgical simulation could accelerate learning curves for surgeons in training. Future research must focus on standardization of AI and XR study reporting, robust databases that are ethically and data protection-compliant, and development of inter-disciplinary tools for various healthcare applications and systems. Full article

More recently, artificial intelligence (AI)-generated content (AIGC) is fundamentally transforming multiple sectors, including materials discovery, healthcare, education, scientific research, and industrial manufacturing. As for the complexities and challenges of intelligent robot design, AIGC has the potential to offer a new paradigm, assisting in conceptual and technical design, functional module design, and the training of the perception ability to accelerate prototyping. Taking the design of an apple-harvesting robot, for example, we demonstrate a basic framework of the AIGC-assisted robot design methodology, leveraging the generation capabilities of available multimodal large language models, as well as the human intervention to alleviate AI hallucination and hidden risks. Second, we study the enhancement effect on the robot perception system using the generated apple images based on the large vision-language models to expand the actual apple images dataset. Further, an apple-harvesting robot prototype based on an AIGC-aided design is demonstrated and a pick-up experiment in a simulated scene indicates that it achieves a harvesting success rate of 92.2% and good terrain traversability with a maximum climbing angle of 32°. According to the tentative research, although not an autonomous design agent, the AIGC-driven design workflow can alleviate the significant complexities and challenges of intelligent robot design, especially for beginners or young engineers. Full article

Background/Objective: Dry eye syndrome affects 16 million Americans with USD 52 billion in annual healthcare costs. With large language models (LLMs) increasingly used for healthcare information, understanding their performance in delivering equitable dry eye guidance across diverse populations is critical. This study aims to evaluate and compare five major LLMs (Grok, ChatGPT, Gemini, Claude.ai, and Meta AI) regarding dry eye syndrome information delivery across different demographic groups. Methods: LLMs were queried using standardized prompts simulating a 62-year-old patient with dry eye symptoms across four demographic categories (White, Black, East Asian, and Hispanic males and females). Responses were analyzed for word count, readability, cultural sensitivity scores (0–3 scale), keyword coverage, and response times. Results: Significant variations existed across LLMs. Word counts ranged from 32 to 346 words, with Gemini being the most comprehensive (653.8 ± 96.2 words) and Claude.ai being the most concise (207.6 ± 10.8 words). Cultural sensitivity scores revealed Grok demonstrated highest awareness for minority populations (scoring 3 for Black and Hispanic demographics), while Meta AI showed minimal cultural tailoring (0.5 ± 0.5). All models recommended specialist consultation, but medical term coverage varied significantly. Response times ranged from 7.41 s (Meta AI) to 25.32 s (Gemini). Conclusions: While all LLMs provided appropriate referral recommendations, substantial disparities exist in cultural sensitivity, content depth, and information delivery across demographic groups. No LLM consistently addressed the full spectrum of dry eye causes across all demographics. These findings underscore the importance for physician oversight and standardization in AI-generated healthcare information to ensure equitable access and prevent care delays. Full article

Background/Objectives: Effective teamwork is crucial for minimizing human error in healthcare settings. Medical teams, typically composed of physicians and nurses, supported by auxiliary professionals, achieve better outcomes when they possess strong collaborative competencies. High-quality teamwork is associated with fewer adverse events and complications and lower mortality rates. Based on this background, the objective of this study is to analyze the perception of non-technical skills and immediate learning outcomes in interprofessional simulation settings based on E-CRM items. Methods: A cross-sectional observational study was conducted involving participants from the official postgraduate Medicine and Nursing programs at the Catholic University of Murcia (UCAM) during the 2024–2025 academic year. Four interprofessional E-CRM simulation sessions were planned, involving randomly assigned groups with proportional representation of medical and nursing students. Teams worked consistently throughout the training and participated in clinical scenarios observed via video transmission by their peers. Post-scenario debriefings followed INACSL guidelines and employed the PEARLS method. Results: Findings indicate that 48.3% of participants had no difficulty identifying the team leader, while 51.7% reported minor difficulty. Role assignment posed moderate-to-high difficulty for 24.1% of respondents. Communication, situation awareness, and early help-seeking were generally managed with ease, though mobilizing resources remained a challenge for 27.5% of participants. Conclusions: This study supports the value of interprofessional education in developing essential competencies for handling urgent, emergency, and high-complexity clinical situations. Strengthening interdisciplinary collaboration contributes to safer, more effective patient care. Full article

Background/Objectives: This case study presents the first documented use of a low-cost, simulated, patient-specific three-dimensional (3D) printed model to support presurgical planning for an infant with Apert syndrome in a resource-limited setting. The primary objectives are to (1) demonstrate the value of 3D printing as a simulation tool for preoperative planning in low-resource environments and (2) identify opportunities for future AI-enhanced simulation models in craniofacial surgical planning. Methods: High-resolution CT data were segmented using InVesalius 3, with mesh refinement performed in ANSYS SpaceClaim (version 2021). The cranial model was fabricated using fused deposition modeling (FDM) on a Creality Ender-3 printer with Acrylonitrile Butadiene Styrene (ABS) filament. Results: The resulting 3D-printed simulated model enabled the surgical team to assess cranial anatomy, simulate incision placement, and rehearse osteotomies. These steps contributed to a reduction in operative time and fewer complications during surgery. Conclusions: This case demonstrates the value of accessible 3D printing as a simulation tool in surgical planning within low-resource settings. Building on this success, the study highlights potential points for AI integration, such as automated image segmentation and model reconstruction, to increase efficiency and scalability in future 3D-printed simulation models. Full article

Fall detection is a critical task in healthcare monitoring systems, especially for elderly populations, for whom timely intervention can significantly reduce morbidity and mortality. This study proposes a privacy-preserving and scalable fall-detection framework that integrates federated learning (FL) with transfer learning (TL) to train deep learning models across decentralized data sources without compromising user privacy. The pipeline begins with data acquisition, in which annotated video-based fall-detection datasets formatted in YOLO are used to extract image crops of human subjects. These images are then preprocessed, resized, normalized, and relabeled into binary classes (fall vs. non-fall). A stratified 80/10/10 split ensures balanced training, validation, and testing. To simulate real-world federated environments, the training data is partitioned across multiple clients, each performing local training using pretrained CNN models including MobileNetV2, VGG16, EfficientNetB0, and ResNet50. Two FL topologies are implemented: a centralized server-coordinated scheme and a ring-based decentralized topology. During each round, only model weights are shared, and federated averaging (FedAvg) is applied for global aggregation. The models were trained using three random seeds to ensure result robustness and stability across varying data partitions. Among all configurations, decentralized MobileNetV2 achieved the best results, with a mean test accuracy of 0.9927, F1-score of 0.9917, and average training time of 111.17 s per round. These findings highlight the model’s strong generalization, low computational burden, and suitability for edge deployment. Future work will extend evaluation to external datasets and address issues such as client drift and adversarial robustness in federated environments. Full article

End-users in a decision-oriented Internet of Things (IoT) healthcare system are often left in the dark regarding critical security information necessary for making informed decisions about potential risks. This is partly due to the lack of transparency and system security awareness end-users have in such systems. To empower end-users and enhance their cybersecurity situational awareness, it is imperative to thoroughly document and report the runtime security controls in place, as well as the security-relevant aspects of the devices they rely on, while the need for better transparency is obvious, it remains uncertain whether current systems offer adequate security metadata for end-users and how future designs can be improved to ensure better visibility into the security measures implemented. To address this gap, we conducted table-top exercises with ten security and ICT experts to evaluate a typical IoT-Health scenario. These exercises revealed the critical role of security metadata, identified the available ones to be presented to users, and suggested potential enhancements that could be integrated into system design. We present our observations from the exercises, highlighting experts’ valuable suggestions, concerns, and views, backed by our in-depth analysis. Moreover, as a proof-of-concept of our study, we simulated three relevant use cases to detect cyber risks. This comprehensive analysis underscores critical considerations that can significantly improve future system protocols, ensuring end-users are better equipped to navigate and mitigate security risks effectively. Full article

Respiratory diseases represent a persistent global health challenge, underscoring the need for intelligent, accurate, and personalized diagnostic and therapeutic systems. Existing methods frequently suffer from limitations in diagnostic precision, lack of individualized treatment, and constrained adaptability to complex clinical scenarios. To address these challenges, our study introduces a modular AI-powered framework that integrates an audio-based disease classification model with simulated molecular biomarker profiles to evaluate the feasibility of future multimodal diagnostic extensions, alongside a synthetic-data-driven prescription recommendation engine. The disease classification model analyzes respiratory sound recordings and accurately distinguishes among eight clinical classes: bronchiectasis, pneumonia, upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), asthma, chronic obstructive pulmonary disease (COPD), bronchiolitis, and healthy respiratory state. The proposed model achieved a classification accuracy of 99.99% on a holdout test set, including 94.2% accuracy on pediatric samples. In parallel, the prescription module provides individualized treatment recommendations comprising drug, dosage, and frequency trained on a carefully constructed synthetic dataset designed to emulate real-world prescribing logic.The model achieved over 99% accuracy in medication prediction tasks, outperforming baseline models such as those discussed in research. Minimal misclassification in the confusion matrix and strong clinician agreement on 200 prescriptions (Cohen’s $\kappa$ = 0.91 [0.87–0.94] for drug selection, 0.78 [0.74–0.81] for dosage, 0.96 [0.93–0.98] for frequency) further affirm the system’s reliability. Adjusted clinician disagreement rates were 2.7% (drug), 6.4% (dosage), and 1.5% (frequency). SHAP analysis identified age and smoking as key predictors, enhancing model explainability. Dosage accuracy was 91.3%, and most disagreements occurred in renal-impaired and pediatric cases. However, our study is presented strictly as a proof-of-concept. The use of synthetic data and the absence of access to real patient records constitute key limitations. A trialed clinical deployment was conducted under a controlled environment with a positive rate of satisfaction from experts and users, but the proposed system must undergo extensive validation with de-identified electronic medical records (EMRs) and regulatory scrutiny before it can be considered for practical application. Nonetheless, the findings offer a promising foundation for the future development of clinically viable AI-assisted respiratory care tools. Full article

Background and Objectives: Peripheral vascular access in infants is a frequent but technically challenging procedure due to the anatomical characteristics of this population. Repeated failed attempts may increase complications and emotional stress for both patients and healthcare professionals. This systematic review aimed to evaluate the efficacy and safety of ultrasound-guided peripheral vascular cannulation compared to the conventional or “blind” technique in infants. Materials and Methods: A systematic review was conducted in accordance with PRISMA guidelines. The PubMed database was searched for studies published between 2017 and 2025. Studies comparing both techniques in infants under two years of age were selected, evaluating variables such as the number of punctures, first-attempt success, healthcare staff perception, associated stress, and the role of simulation in training. Results: Eleven studies were included, comprising clinical trials, observational studies, and training program assessments from different countries. Most reported a higher first-attempt success rate with the ultrasound-guided technique (often exceeding 85%), along with fewer punctures and complications, particularly among less-experienced professionals. Improvements in staff perception were also observed following structured training. The impact on stress experienced by patients and families was less frequently assessed directly, although some studies reported indirect benefits. Conclusions: Ultrasound-guided peripheral vascular cannulation appears to be more effective and safer than the conventional technique in infants, particularly in complex or critical care contexts. Its implementation requires specific training and appropriate resources but could significantly improve clinical outcomes and the pediatric patient experience. Full article

This study applies stochastic discrete event modeling to demonstrate that reducing wait times for specialized outpatient clinics in the Costa Rican public healthcare system is possible. The classification process identified four medical specialties with the longest wait times. It includes the creation of a separate queuing theory model for each specialty. The birth and death model allowed for estimating the number of arrivals and consultations in the simulation. Validation was performed by comparing the model’s input and output data with real-world statistical reports. An analysis of medical specialists revealed that approximately 22% of patients referred to secondary care did not require specialized medical consultation. Through simulation and the use of stochastic input data, patient waiting times decreased. In an optimistic scenario, waiting times decreased steadily across all specialties over 24 months. Ophthalmology and orthopedics reduced their waiting times to less than 300 days. Otorhinolaryngology decreased from 370 to 250 days, and urology showed the most significant improvement, decreasing from 350 to 100 days in the first year and remaining stable. This evidence transforms the traditional paradigm of increasing capacity as the only solution to the waiting list problem and positions improving the referral process as an alternative. To achieve these results, the study highlights the importance of implementing improved triage protocols in primary care, integrating decision-support tools for general practitioners using machine learning, for example, to reduce unnecessary referrals. Training programs and feedback mechanisms could also align referral practices with specialty criteria. While these strategies were not implemented in this study, the simulation results provide a solid basis for their design and future evaluation. Full article