Search Results (1,328)

How do regulatory policies, funding structures, and cross-sector coordination shape knowledge flows and institutional transformation? Focusing on the smart medical device sector in Taiwan, this study explores how governance dynamics accelerate system transformation and foster demand for adaptive and integrative innovation systems. Building on the National Biotechnology Innovation System framework and qualitative system dynamics modeling, the study analyzes institutional interactions through 28 semi-structured interviews and 18 policy documents. Findings identify systemic bottlenecks, including translational gaps, coordination challenges, and barriers for traditional manufacturers. These gaps have enabled tech firms to emerge as system leaders by bridging these institutional gaps. This study extends innovation systems theory by conceptualizing an emergent governance function that addresses institutional gaps. At the policy level, the study highlights the importance of enabling institutional change in governance to address structural fragmentation and support system-wide transformation. 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

Topical drug administration is a common method of delivering medications to the eye to treat various ocular conditions, including glaucoma, dry eye, and inflammation. Drug efficacy following topical administration, including the drug’s distribution within the eye, absorption and elimination rates, and physiological responses can be predicted using physiologically based pharmacokinetic (PBPK) modeling. High-resolution computational models of the eye are desirable to improve simulations of drug delivery; however, these approaches can have long run times. In this study, a fast-running computational quasi-3D (Q3D) model of the human tear film was developed to account for absorption, blinking, drainage, and evaporation. Visualization of blinking mechanics and flow distributions throughout the tear film were enabled using this Q3D approach. Average drug absorption throughout the tear film subregions was quantified using a high-resolution compartment model based on a system of ordinary differential equations (ODEs). Simulations were validated by comparing them with experimental data from topical administration of 0.1% dexamethasone suspension in the tear film (R² = 0.76, RMSE = 8.7, AARD = 28.8%). Overall, the Q3D tear film model accounts for critical mechanistic factors (e.g., blinking and drainage) not previously included in fast-running models. Further, this work demonstrated methods toward improved computational efficiency, where central processing unit (CPU) time was decreased while maintaining accuracy. Building upon this work, this Q3D approach applied to the tear film will allow for more seamless integration into full-body models, which will be an extremely valuable tool in the development of treatments for ocular conditions. 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

The complex microenvironment of wounds, along with challenges such as microbial infections, tissue damage, and inflammatory responses during the healing process, renders wound repair a complex medical issue. Owing to their ease of administration, effective outcomes, and painless application, biomacromolecule-based wound dressings have become a focal point in current clinical research. In recent years, hydrogels, microneedles, and electrospun nanofibers have emerged as three novel types of wound dressings. By influencing various stages of healing, they have notably enhanced chronic wound healing outcomes and hold considerable potential for wound repair applications. This review describes the preparation methods, classification, and applications of hydrogels, microneedles, and electrospun nanofibers around the various stages of wound healing, clarifying the healing-promoting mechanisms and characteristics of the three methods in different stages of wound healing. Building upon this foundation, we further introduce smart responsiveness, highlighting the application of stimuli-responsive wound dressings in dynamic wound management, aiming to provide insights for future research. Full article

This study builds on Davis’ TAM by integrating environmental and psychological variables relevant to AI medical diagnostics. This study developed a mathematical theoretical model called the “AI medical diagnosis-acceptance evaluation model” (AMD-AEM) to better understand acceptance behavior intention. Using mathematical modeling, we established reflective measurement model indicators and structural equation relationships, where linear structural equations illustrate the interactions among latent variables. In 2025, we collected empirical data from 2380 patients and medical staff who have experience with AI diagnostic systems in teaching hospitals in central Taiwan. Smart PLS 3 was employed to validate the AMD-AEM model. The results reveal that perceived usefulness (PU) and information quality (IQ) are the primary predictors of acceptance behavior intention (ABI). Additionally, perceived ease of use (PE) indirectly influences ABI through PU and attitude toward use (ATU). AI emotional perception (AEP) notably shows a significant positive relationship with ATU, highlighting that warm and positive human–AI interactions are crucial for user acceptance. IQ was identified as a mediating variable, with variance accounted for (VAF) coefficient analysis confirming its complete mediation effect on the path from ATU to ABI. This indicates that information quality enhances user attitudes and directly increases acceptance behavior intention. The AMD-AEM model demonstrates an excellent fit, providing valuable insights for academia and the healthcare industry. Full article

In recent years, the biomedical startup industry has flourished, and yet, it still faces challenges in adapting to changing market demands. Meanwhile, the widespread use of single-use medical devices generates significant waste, posing threats to environmental sustainability. Addressing this issue has become a critical challenge for humanity today. The study aimed to delve into the specific difficulties faced by Taiwanese biomedical entrepreneurs during the innovation and development of medical devices from a sustainability perspective and to explore solutions. This study collected first-hand experiences and insights from Taiwanese biomedical entrepreneurs through a literature review and expert questionnaires. It employed Interpretive Structural Modeling to analyze the development stages and interrelationships of biomedical device startups for building sustainable biomedical innovation. The Clinical Needs Assessment is revealed as the most influential factor, shaping Regulatory Feasibility Evaluation, Clinical Trial Execution, and Market Access Compliance. Our findings provide a structured problem-solving framework to assist biomedical startups in overcoming challenges while incorporating energy-saving and carbon reduction processes to achieve environment sustainability goals. The results of this study show that biomedical innovation practitioners should prioritize integrating sustainability considerations directly into the earliest stage of a Clinical Needs Assessment. Full article

In the modern world, there is a need to provide a better understanding of the importance or relevance of the available descriptive features for predicting target attributes to solve the feature ranking problem. Among the published works, the vast majority are devoted to the problems of feature selection and extraction, and not the problems of their ranking. In this paper, we propose a novel method based on the Bayesian approach that allows us to not only to build a methodically justified way of ranking features on small datasets, but also to methodically solve the problem of benchmarking the results obtained by various ranking algorithms. The proposed method is also model-free, since no restrictions are imposed on the model. We carry out an experimental comparison of our proposed method with the classical frequency method. For this, we use two synthetic datasets and two public medical datasets. As a result, we show that the proposed ranking method has a high level of self-consistency (stability) already at the level of 50 samples, which is greatly improved compared to classical logistic regression and SHAP ranking. All the experiments performed confirm our theoretical conclusions: with the growth of the sample, an increasing trend of mutual consistency is observed, and our method demonstrates at least comparable results, and often results superior to other methods in the values of self-consistency and monotonicity. The proposed method can be applied to a wide class of rankings of influence factors on small samples, including industrial tasks, forensics, psychology, etc. Full article

As diversity, equity, and inclusion (DEI) efforts in higher education face increasing political resistance, it is critical to understand how equity-centered institutional change is fostered, and who is transformed in the process. This study examines the intended and emergent outcomes of faculty professional development initiatives implemented through the Howard Hughes Medical Institute’s Inclusive Excellence (HHMI IE) program. We analyzed annual institutional reports and anonymous reflections from four public universities in a regional Peer Implementation Cluster (PIC), focusing on how change occurred at individual, community, and institutional levels. Guided by Kezar’s Shared Equity Leadership (SEL) framework, our thematic analysis revealed that while initiatives were designed to improve student outcomes through inclusive pedagogy, the most profound outcome was the development of equity consciousness among faculty. Defined as a growing awareness of systemic inequities and a sustained commitment to address them, equity consciousness emerged as the most frequently coded theme across all levels of change. These findings suggest that equity-centered faculty development can serve as a catalyst for institutional transformation, not only by shifting teaching practices but also by building distributed leadership and deeper organizational engagement with equity. This effort also emphasizes that documenting emergent outcomes is essential for recognizing the holistic impact of sustained institutional change. Full article

Objective: To explore the experiences and self-efficacy of parent-caregivers providing care for a child with a tracheostomy tube. Study Design: Parent-caregivers completed surveys and participated in semi-structured interviews about their experiences learning to care for their child with a tracheostomy tube. Survey data were analyzed using descriptive statistics. Interviews were transcribed verbatim and analyzed thematically through coding. Results: Fifteen parent-caregivers participated in the survey, 13 of whom completed an interview. After receiving a tracheostomy, children were hospitalized a median of 6 months prior to discharge home. At the time of our study, children had been home for a median of 3.5 years. Parent-caregivers felt more prepared to perform routine daily care compared to triaging a change in medical status. Parent-caregiver self-efficacy in performing tracheostomy care skills improved with experience at home. Four themes were identified from interviews: new identity formation, enduring education, child and family biopsychosocial support, and establishing normalcy. Parent-caregivers shared that education was more than just acquiring skills; it also involved discovering diverse ways of learning and building confidence in one’s own abilities to fulfill the many types of roles they serve to successfully care for and keep their child safe while supporting their social and emotional needs as parent-caregivers. Conclusions: Parent-caregivers’ reflections on their experiences provide critical insight into their psychosocial needs and challenges in providing care to children with tracheostomies. Further investigation of lived experiences is vital to shaping a community that can support families of medically complex children. Full article

The Internet of Medical Things (IoMT) is revolutionizing healthcare by integrating smart diagnostic devices with cloud computing and real-time data analytics. The emergence of infectious diseases, including COVID-19, underscores the need for rapid and decentralized diagnostics to facilitate early intervention. Traditional centralized laboratory testing introduces delays, limiting timely medical responses. While point-of-care molecular diagnostic (POC-MD) systems offer an alternative, challenges remain in cost, accessibility, and network inefficiencies. This study proposes an IoMT-based architecture for fully automated POC-MD devices, leveraging WebSockets for optimized communication, enhancing microfluidic cartridge efficiency, and integrating a hardware-based emulator for real-time validation. The system incorporates DNA extraction and real-time polymerase chain reaction functionalities into modular, networked components, improving flexibility and scalability. Although the system itself has not yet undergone clinical validation, it builds upon the core cartridge and detection architecture of a previously validated cartridge-based platform for Chlamydia trachomatis and Neisseria gonorrhoeae (CT/NG). These pathogens were selected due to their global prevalence, high asymptomatic transmission rates, and clinical importance in reproductive health. In a previous clinical study involving 510 patient specimens, the system demonstrated high concordance with a commercial assay with limits of detection below 10 copies/μL, supporting the feasibility of this architecture for point-of-care molecular diagnostics. By addressing existing limitations, this system establishes a new standard for next-generation diagnostics, ensuring rapid, reliable, and accessible disease detection. Full article

Background/Objectives: Accurate classification of brain tumors is critical for treatment planning and prognosis. While deep convolutional neural networks (CNNs) have shown promise in medical imaging, few studies have systematically compared multiple architectures or integrated ensemble strategies to improve diagnostic performance. This study aimed to evaluate various CNN models and optimize classification performance using a majority voting ensemble approach on T1-weighted MRI brain images. Methods: Seven pretrained CNN architectures were fine-tuned to classify four categories: glioblastoma, meningioma, pituitary adenoma, and no tumor. Each model was trained using two optimizers (SGDM and ADAM) and evaluated on a public dataset split into training (70%), validation (10%), and testing (20%) subsets, and further validated on an independent external dataset to assess generalizability. A majority voting ensemble was constructed by aggregating predictions from all 14 trained models. Performance was assessed using accuracy, Kappa coefficient, true positive rate, precision, confusion matrix, and ROC curves. Results: Among individual models, GoogLeNet and Inception-v3 with ADAM achieved the highest classification accuracy (0.987). However, the ensemble approach outperformed all standalone models, achieving an accuracy of 0.998, a Kappa coefficient of 0.997, and AUC values above 0.997 for all tumor classes. The ensemble demonstrated improved sensitivity, precision, and overall robustness. Conclusions: The majority voting ensemble of diverse CNN architectures significantly enhanced the performance of MRI-based brain tumor classification, surpassing that of any single model. These findings underscore the value of model diversity and ensemble learning in building reliable AI-driven diagnostic tools for neuro-oncology. Full article

The integration of artificial intelligence into healthcare has advanced rapidly in recent years, with multimodal approaches emerging as promising tools for improving diagnostic accuracy and clinical decision making. These approaches combine heterogeneous data sources such as medical images, electronic health records, physiological signals, and clinical notes to better capture the complexity of disease processes. Despite this progress, only a limited number of studies offer a unified view of multimodal AI applications in medicine. In this review, we provide a comprehensive and up-to-date analysis of machine learning and deep learning-based multimodal architectures, fusion strategies, and their performance across a range of diagnostic tasks. We begin by summarizing publicly available datasets and examining the preprocessing pipelines required for harmonizing heterogeneous medical data. We then categorize key fusion strategies used to integrate information from multiple modalities and overview representative model architectures, from hybrid designs and transformer-based vision-language models to optimization-driven and EHR-centric frameworks. Finally, we highlight the challenges present in existing works. Our analysis shows that multimodal approaches tend to outperform unimodal systems in diagnostic performance, robustness, and generalization. This review provides a unified view of the field and opens up future research directions aimed at building clinically usable, interpretable, and scalable multimodal diagnostic systems. Full article

Bilingual communication is increasingly prevalent in this globally connected world, where cultural exchanges and international interactions are unavoidable. Existing automatic speech recognition (ASR) systems are often limited to single languages. However, the growing demand for bilingual ASR in human–computer interactions, particularly in medical services, has become indispensable. This article addresses this need by creating an application programming interface (API)-based platform using VOSK, a popular open-source single-language ASR toolkit, to efficiently deploy a self-evaluated bilingual ASR system that seamlessly handles both primary and secondary languages in tasks like Mandarin–English mixed-speech recognition. The mixed error rate (MER) is used as a performance metric, and a workflow is outlined for its calculation using the edit distance algorithm. Results show a remarkable reduction in the Mandarin–English MER, dropping from ∼65% to under 13%, after implementing the self-evaluation framework and mixed-language algorithms. These findings highlight the importance of a well-designed system to manage the complexities of mixed-language speech recognition, offering a promising method for building a bilingual ASR system using existing monolingual models. The framework might be further extended to a trilingual or multilingual ASR system by preparing mixed-language datasets and computer development without involving complex training. Full article

Maternal mortality and morbidity are critical health challenges in the U.S., and building the perinatal workforce is a key to providing high-quality maternal medical care and services. Texas Southern University (TSU), home to a Doctor of Pharmacy program, launched the first Maternal Health Education and Research Training (MHERT) program to educate a cohort of high school, pharmacy, and health sciences students. Aiming to raise awareness of maternal health issues, build research skills, and promote action-based solutions. MHERT integrated online self-paced interactive lessons with hands-on research or community projects. Topics included maternal health epidemiology, causes of morbidity and mortality, research methods, literature reviews, and the development of action plans addressing maternal health challenges. Assessment tools included quizzes, open-ended reflection responses, training surveys, and course evaluations. Running from 3 June to 26 July 2024, the program enrolled 22 students. All participants completed both course components. Course evaluations showed strong and consistent satisfaction with the program, with teaching effectiveness rated at 95% and 96% for mid-program and final evaluations, respectively. MHERT enhanced participants’ understanding of maternal health, improved research skills, and encouraged community engagement and interdisciplinary collaboration. It offers a scalable model to strengthen public health education among high school, pharmacy, and health sciences students. Full article