Search Results (74)

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a worldwide challenge. There are an estimated 17–24 million patients worldwide, with an estimated 60 percent or more who have not been diagnosed. Without a known cure, no specific curative medication, disability lasting years to being life-long, and disagreement among healthcare providers as to how to most appropriately treat these patients, ME/CFS patients are in need of assistance. Appropriate healthcare provider education would increase the percentage of patients diagnosed and treated; however, in-school healthcare provider education is limited. To address the latter issue, the New Jersey Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Association (NJME/CFSA) has developed an independent, incentive-driven, learning program for students of the health professions. NJME/CFSA offers a yearly scholarship program in which applicants write a scholarly paper on an ME/CFS-related topic. The efficacy of the program is demonstrated by the 2024–2025 first place scholarship winner’s essay, which addresses the biological basis of ME/CFS and how the healthcare provider can improve the quality of life of ME/CFS patients. For the reader, the essay provides an update on what is known regarding the biological underpinnings of ME/CFS, as well as a medical student’s perspective as to how the clinician can provide care and support for ME/CFS patients. The original essay has been slightly modified to demonstrate that ME/CFS is a worldwide problem and for publication. Full article

During the coronavirus 2019 pandemic, sports activities were restricted, raising concerns about their impact on the physical condition of adolescent athletes, which remained largely unquantified. This study was designed with two primary objectives: first, to precisely quantify and elucidate the differences in the physical condition of adolescent athletes before and after activity restrictions due to the pandemic; and second, to innovatively develop and validate a non-contact medical checkup application. Medical checks were conducted on 563 athletes designated for sports enhancement. Participants were junior high school students aged 13 to 15, and the sample consisted of 315 boys and 248 girls. Furthermore, we developed a smartphone application and compared self-checks using the application with in-person checks by orthopedic surgeons to determine the challenges associated with self-checks. Statistical tests were conducted to determine whether there were statistically significant differences in range of motion and flexibility parameters before and after the pandemic. Additionally, items with discrepancies between values self-entered by athletes using the smartphone application and values measured by specialists were detected, and application updates were performed. Student’s t-test was used for continuous variables, whereas the chi-square test was used for other variables. Following the coronavirus 2019 pandemic, athletes were stiffer than during the pre-pandemic period in terms of hip and shoulder joint rotation range of motion and heel–buttock distance. The dominant hip external rotation decreased from 53.8° to 46.8° (p = 0.0062); the non-dominant hip external rotation decreased from 53.5° to 48.0° (p = 0.0252); the dominant shoulder internal rotation decreased from 62.5° to 54.7° (p = 0.0042); external rotation decreased from 97.6° to 93.5° (p = 0.0282), and the heel–buttock distance increased from 4.0 cm to 10.4 cm (p < 0.0001). The heel–buttock distance and straight leg raising angle measurements differed between the self-check and face-to-face check. Although there are items that cannot be accurately evaluated by self-check, physical condition can be improved with less contact by first conducting a face-to-face evaluation under appropriate guidance and then conducting a self-check. These findings successfully address our primary objectives. Specifically, we demonstrated a significant decline in the physical condition of adolescent athletes following pandemic-related activity restrictions, thereby quantifying their impact. Furthermore, our developed non-contact medical checkup application proved to be a viable tool for monitoring physical condition with reduced contact, although careful consideration of measurable parameters is crucial. This study provides critical insights into the long-term effects of activity restrictions on young athletes and offers a practical solution for health monitoring during infectious disease outbreaks, highlighting the potential for hybrid checkup approaches. Full article

Background: A detailed understanding of cardiac anatomy and physiology is crucial in cardiovascular medicine. However, traditional learning methods often fall short in addressing this complexity. Augmented reality (AR) offers a promising tool to enhance comprehension. To assess its potential integration into the Heidelberger Curriculum Medicinale (HeiCuMed), we conducted a needs assessment among medical students and lecturers at Heidelberg University Medical School. Methods: Our survey aimed to evaluate the perceived benefits of AR-based learning compared to conventional methods and to gather expectations regarding an AR course in cardiovascular medicine. Using LimeSurvey, we developed a questionnaire to assess participants’ prior AR experience, preferred learning methods, and interest in a proposed AR-based, 2 × 90-min in-person course. Results: A total of 101 students and 27 lecturers participated. Support for AR in small-group teaching was strong: 96.3% of students and 90.9% of lecturers saw value in a dedicated AR course. Both groups favored its application in anatomy, cardiac surgery, and internal medicine. Students prioritized congenital heart defects, coronary anomalies, and arrhythmias, while lecturers also emphasized invasive valve interventions. Conclusions: There is significant interest in AR-based teaching in cardiovascular education, suggesting its potential to complement and improve traditional methods in medical curricula. Further studies are needed to assess the potential benefits regarding learning outcomes. Full article

Background: Asthma is the most common chronic respiratory disease in children and adolescents, associated with high morbidity and healthcare costs. Telemedicine has emerged as a strategy to improve access to care, adherence to treatment and symptom control. However, its effectiveness in the pediatric population has not been clearly studied. Objective: To assess the clinical effectiveness of telemedicine interventions in asthma control and health-related quality of life in asthmatic children and adolescents. Methodology: A systematic review and meta-analysis were performed following PRISMA-2020 guidelines, with previous registration in PROSPERO (CRD42025251000837). Sixteen randomized clinical trials (n = 2642) with patients aged 2–18 years were included. The interventions included videoconferencing, mobile applications, web systems, interactive voice response and mobile units in schools. The outcomes were measured using validated quality-of-life (PAQLQ) and asthma control (ACT/c-ACT) questionnaires. Results: Seven studies were incorporated into the PAQLQ meta-analysis, whose overall effect was non-significant (mean difference = 0.06; 95% CI: −0.06 to 0.18; I² = 5.7%). Five studies provided ACT/c-ACT data, showing a significant effect in favor of telemedicine (mean difference = 0.61; 95% CI: 0.32 to 0.90; I² = 73%). Complementary qualitative analysis revealed improvements in adherence, reduction in exacerbations, emergency department visits and use of rescue medication. Conclusions: Telemedicine improves the clinical control of pediatric asthma, although its impact on the quality of life is limited. Multicenter trials with long-term follow-up and cost-effectiveness evaluation are required. Full article

Background: Nursing decision-making is pivotal for patient safety and care quality. While artificial intelligence (AI) offers transformative potential in this field, a comprehensive analysis of global research trends is lacking. Methods: We conducted a bibliometric analysis of 238 publications (197 research papers, 41 reviews) from the Web of Science Core Collection (2003–2025) using CiteSpace and VOSviewer. Results: The results reveal growing interest (7.59% annually) in the field of AI in nursing decision-making, with contributions from 54 countries/regions. The USA leads in the number of publications, followed by China and Canada, while the United Kingdom stands out in terms of citation impact. Institutions such as Columbia University and Harvard Medical School dominate in both the publication volume and citation frequency. Journal analysis shows that the top three journals in terms of publication volume in this field are Cin-Computers Informatics Nursing, Journal of Nursing Management, and Applied Clinical Informatics. Keyword analysis highlights the significant potential of natural language processing technologies, particularly those based on large language models (e.g., ChatGPT), in nursing decision-making. Furthermore, emerging trends are evident, with the sudden appearance and rapid growth of keywords such as “patient safety” and “user acceptance”, indicating a shift in research focus from purely technology-driven studies to a greater emphasis on the practical impact of AI technologies on nursing systems and their clinical applications. Conclusions: This study delineates the current landscape and evolving trends of AI in nursing decision-making, emphasizing its progression from theoretical frameworks to clinical integration, thereby providing valuable references for future research. Full article

The medical profession is a prestigious position that requires very extensive higher education, to which only a small proportion of applicants are accepted. Changes in selection criteria can profoundly impact applicants’ pre-educational choices, early medical studies, and the characteristics of future medical professionals. This study assesses the impact of changing the admission requirements of medical schools in Finland. We examined two cohorts of students admitted to the University of Oulu’s medical school: 2009–2011 (n = 316) and 2013–2015 (n = 339). The first cohort prepared for the entrance exam with a field-specific book, while the second cohort focused on secondary school subjects such as biology, chemistry, and physics. We analysed the effects of the changes on accepted students’ profiles and the relationship between their prior performance, entrance exam success, and performance in medical studies. Changing the entrance exam content did not significantly alter accepted students’ profiles or ease access for recent matriculants. However, minor changes in correlations between prior performance, entrance exam performance, and medical study success were observed. The entrance exam’s predictive power for academic success was weak in both admission periods. This comparative study found that changing the entrance exam material did not notably influence the characteristics of accepted students. The changes to the selection criteria appear to have a minor impact on the actual success of students studying medicine. Regardless of the selection criteria, those who are accepted typically demonstrate strong learning capabilities. Despite modifications in the required entry-level knowledge, students with strong skills are admitted. Full article

Artificial intelligence, the simulation of human intelligence by computers and machines, has found its way into healthcare, helping surgeons, doctors, radiologists, and many more. However, over 80% of healthcare professionals consists of people working in allied health professions such as nurses, physiotherapists, and midwives. Considering the aging of the general population around the world, the workforce shortages in these occupations are especially crucial. As the COVID-19 pandemic demonstrated, globally, most healthcare systems are strained, and there is a consensus that current healthcare systems are not sustainable with the increasing challenges. AI is often viewed as one of the potential solutions for not only reducing the strain on the healthcare workforce, but also to sustain the current workforce. Still, most AI applications are being developed for the medical community and often allied health is overlooked or not even considered despite comprising a large proportion of the total workforce. In addition, the interest of the private sector to invest specifically in the allied health workforce is low since the financial incentive is low. This paper provides examples of AI solutions for seven important allied health professions. To increase the uptake of AI solutions in allied healthcare, AI companies need to connect more with professional associations and be as patient-oriented as many claim to be. There also needs to be more AI schooling for allied healthcare professionals to increase adoption of these AI solutions. Full article

This study introduces the PROCDEC dataset, a novel collection of 1140 cases with 30 cardiovascular risk factors gathered over a 10-year period from school children in Santa Clara, Cuba. The dataset was curated with input from medical experts in pediatric cardiology, endocrinology, general medicine, and clinical laboratory, ensuring its clinical relevance. We conducted a rigorous performance evaluation of 10 machine learning (ML) algorithms to classify cardiovascular risk into two categories: at risk and not at risk. The models were assessed using a stratified k-fold cross-validation approach to enhance the reliability of the findings. Among the evaluated models—Bayes Net, Naive Bayes, SMO, K-Nearest Neighbors (KNN), Logistic Regression, AdaBoost, Multilayer Perceptron (MLP), J48, Logistic Model Tree (LMT), and Random Forest (RF)—the best-performing classifiers (MLP, LMT, J48 and Logistic Regression) achieved F1-score values exceeding 0.83, indicating strong predictive capability. To improve interpretability, we employed feature selection techniques to rank the most influential risk factors. Key contributors to classification performance included hypertension, hyperreactivity, body mass index (BMI), uric acid, cholesterol, parental hypertension, and sibling dyslipidemia. These findings align with established clinical knowledge and reinforce the potential of ML models for pediatric cardiovascular risk assessment. Unlike previous studies, our research not only evaluates multiple ML techniques but also emphasizes their clinical applicability and interpretability, which are critical for real-world implementation. Future work will focus on validating these models with external datasets and integrating them into decision-support systems for early risk detection. Full article

Background/Objectives: Cochlear implantation (CI) significantly enhances speech perception and quality of life in patients with severe-to-profound sensorineural hearing loss, yet outcomes vary substantially. Accurate preoperative prediction of CI outcomes remains challenging. This study aimed to develop and validate a machine learning model predicting postoperative speech recognition using a large, single-center dataset. Additionally, we compared model performance with expert clinical predictions to evaluate potential clinical utility. Methods: We retrospectively analyzed data from 2571 adult patients with postlingual hearing loss who received their cochlear implant between 2000 and 2022 at Hannover Medical School, Germany. A decision tree regression model was trained to predict monosyllabic (MS) word recognition score one to two years post-implantation using preoperative clinical variables (age, duration of deafness, preoperative MS score, pure tone average, onset type, and contralateral implantation status). Model evaluation was performed using a random data split (10%), a chronological future cohort (patients implanted after 2020), and a subset where experienced audiologists predicted outcomes for comparison. Results: The model achieved a mean absolute error (MAE) of 17.3% on the random test set and 17.8% on the chronological test set, demonstrating robust predictive performance over time. Compared to expert audiologist predictions, the model showed similar accuracy (MAE: 19.1% for the model vs. 18.9% for experts), suggesting comparable effectiveness. Conclusions: Our machine learning model reliably predicts postoperative speech outcomes and matches expert clinical predictions, highlighting its potential for supporting clinical decision-making. Future research should include external validation and prospective trials to further confirm clinical applicability. Full article

The International CCN Society has been organizing workshops and conferences for the past two decades to advance our understanding of the biology and pathophysiology of the cellular communication network (CCN) proteins. The 12th CCN Workshop broadened the scope of discussions, introducing topics like CCN-dependent and -independent signaling networks involved in brain development, cellular senescence, efferocytosis, neurobiology, and the application of DNA-fabricated origami structures. This expansion proved fruitful and should continue in future events. Fostering collaborations across various fields has created a dynamic environment for innovative ideas, driving substantial progress to tackle both basic scientific questions and clinically relevant challenges. Three standout presentations sparked significant discussions and highlighted key advancements in these areas. These include the work of Li-Jen Lee (Neurobiology and Cognitive Science Center, National Taiwan University) on the involvement of the CCN2 protein in depressive and aggressive behaviors in mice; the studies of Anna Zampetaki (King’s College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences) and Brahim Chaqour (University of Pennsylvania, Perelman School of Medicine, Dept of Molecular Ophthalmology) on the metabolome and mechanosensing in iPSC-derived human blood vessel organoids and in the microvasculature of genetically modified mice, and the talk of Björn Högberg (Karolinska Institutet, Department of Medical Biochemistry and Biophysics) on the promises of DNA origami. We believe that these examples illustrate better future directions, as they offer an opportune moment to pursue initiatives that broaden the focus of the CCN Workshops and other projects like ARBIOCOM (website link included below) that support collaboration among research societies, educational institutions, and private biomedical industries, all working together to further our understanding of biosignaling and cellular communication networks for the development of new drug discovery methods and disease treatments. Full article

Background/Objectives: The increasing application of artificial intelligence (AI) and machine learning (ML) in health and medicine has attracted a great deal of research interest in recent decades. This study aims to provide a global and historical picture of research concerning AI and ML in health and medicine. Methods: We used the Scopus database for searching and extracted articles published between 2000 and 2024. Then, we generated information about productivity, citations, collaboration, most impactful research topics, emerging research topics, and author keywords using Microsoft Excel 365 and VOSviewer software (version 1.6.20). Results: We retrieved a total of 22,113 research articles, with a notable surge in research activity in recent years. Core journals were Scientific Reports and IEEE Access, and core institutions included Harvard Medical School and the Ministry of Education of the People’s Republic of China, while core countries comprised the United States, China, India, the United Kingdom, and Saudi Arabia. Citation trends indicated substantial growth and recognition of AI’s and ML impact on health and medicine. Frequent author keywords identified key research hotspots, including specific diseases like Alzheimer’s disease, Parkinson’s diseases, COVID-19, and diabetes. The author keyword analysis identified “deep learning”, “convolutional neural network”, and “classification” as dominant research themes. Conclusions: AI’s transformative potential in AI and ML in health and medicine holds promise for improving global health outcomes. Full article

Background/Objectives: Eggs, a nutritious and affordable food, are not widely consumed by adolescents, who show many nutrient inadequacies. Modeling dietary substitutions with eggs and their costs can provide dietary insights while considering economic constraints. This study theoretically modeled the impact of substituting an egg for another protein source, considering nutrient quality and cost, using exemplary menus with application to adolescents. Methods: The substitution was modeled in four different seven-day exemplary menus: (1) the Healthy U.S.-Style Dietary Pattern (HUSS), (2) Harvard Medical School’s Heathy Eating Guide, (3) the National Heart, Lung, and Blood Institute’s Dietary Approaches to Stop Hypertension (DASH) diet and (4) the Healthy U.S.-Style Vegetarian Dietary Pattern (HVEG). One egg replaced the gram amount and nutrient profile of a protein source food in each menu. Micronutrient quality was assessed using the Food Nutrient Index (FNI), scored 0–100. The Center for Nutrition Policy and Promotion Food Price Database informed the food prices. Pairwise t-tests compared the effects of egg substitution on micronutrient scores and daily costs. Results: The daily egg substitution increased FNI scores for choline and vitamin D in the HUSS (83 to 95 and 69 to 75, respectively), DASH (80 to 91 and 55 to 59, respectively), and HVEG (91 to 100 and 44 to 51, respectively), and choline alone (89 to 98) in the Harvard menu. Daily menu prices were not significantly different after the egg substitution (p > 0.01). Conclusions: Substituting one egg for another protein source food increased the micronutrient quality of choline and vitamin D in exemplary menus without increasing the cost; however, factors such as food preferences and the economic accessibility of eggs in different contexts should also be considered. Full article

The increasing digitalisation of healthcare has created a pressing need for health professionals to develop robust digital skills. This paper explores the imperative of equipping health professionals with the necessary digital proficiency to enhance their daily workflows and improve patient care. The expanding use of digital technologies, including electronic health records, telehealth, and artificial intelligence, has transformed the healthcare landscape. However, the adoption of these technologies has been hindered by barriers, such as a lack of interoperability and hesitancy among healthcare providers. To address these challenges, this paper argues that digital skill development must be a core component of healthcare education and professional training. Medical schools and healthcare organisations must prioritise the integration of digital health curricula and continuous learning opportunities to ensure that the next generation of healthcare providers is well-equipped to navigate the digital healthcare ecosystem. Additionally, this paper highlights the importance of fostering a culture of digital innovation and collaboration within healthcare settings. By empowering health professionals to actively participate in the development and testing of new digital health applications, the industry can unlock the full potential of digital technologies to enhance daily workflows and improve patient outcomes. Full article

Background: Artificial intelligence (AI) has recently made unprecedented contributions in every walk of life, but it has not been able to work its way into diagnostic medicine and standard clinical practice yet. Although data scientists, researchers, and medical experts have been working in the direction of designing and developing computer aided diagnosis (CAD) tools to serve as assistants to doctors, their large-scale adoption and integration into the healthcare system still seems far-fetched. Diagnostic radiology is no exception. Imagining techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans have been widely and very effectively employed by radiologists and neurologists for the differential diagnoses of neurological disorders for decades, yet no AI-powered systems to analyze such scans have been incorporated into the standard operating procedures of healthcare systems. Why? It is absolutely understandable that in diagnostic medicine, precious human lives are on the line, and hence there is no room even for the tiniest of mistakes. Nevertheless, with the advent of explainable artificial intelligence (XAI), the old-school black boxes of deep learning (DL) systems have been unraveled. Would XAI be the turning point for medical experts to finally embrace AI in diagnostic radiology? This review is a humble endeavor to find the answers to these questions. Methods: In this review, we present the journey and contributions of AI in developing systems to recognize, preprocess, and analyze brain MRI scans for differential diagnoses of various neurological disorders, with special emphasis on CAD systems embedded with explainability. A comprehensive review of the literature from 2017 to 2024 was conducted using host databases. We also present medical domain experts’ opinions and summarize the challenges up ahead that need to be addressed in order to fully exploit the tremendous potential of XAI in its application to medical diagnostics and serve humanity. Results: Forty-seven studies were summarized and tabulated with information about the XAI technology and datasets employed, along with performance accuracies. The strengths and weaknesses of the studies have also been discussed. In addition, the opinions of seven medical experts from around the world have been presented to guide engineers and data scientists in developing such CAD tools. Conclusions: Current CAD research was observed to be focused on the enhancement of the performance accuracies of the DL regimens, with less attention being paid to the authenticity and usefulness of explanations. A shortage of ground truth data for explainability was also observed. Visual explanation methods were found to dominate; however, they might not be enough, and more thorough and human professor-like explanations would be required to build the trust of healthcare professionals. Special attention to these factors along with the legal, ethical, safety, and security issues can bridge the current gap between XAI and routine clinical practice. Full article

The student–patient relationship represents the cornerstone of medical education, shaping future doctors’ knowledge, skills and attitudes. While most patients allow student involvement in their care, some may express discomfort. Thus, obtaining explicit consent is essential to respect patients’ right of autonomy. This study mainly aims to assess the practical application of informed consent by medical students and teachers regarding students’ presence and participation in patients’ care. An observational cross-sectional study was performed, and an online questionnaire was given to students and teachers at a single medical school, via institutional email. The study included 289 participants, namely 232 students and 57 teachers. While 81% of teachers reported always asking the patient’s consent for students’ presence, only 28% of students claimed this to be the case. Despite challenges like overcrowding and limited time, involving students in healthcare benefits both students and patients. Moreover, medical ethics education is crucial to foster compassionate care and promote ethical reasoning. The disparities found between teachers’ practices and students’ perspectives highlight the need to intervene and provide them with an adequate education on ethical values in clinical practice. Strategic interventions at institutional levels are required for a simultaneous high quality of patient care and clinical training. Full article