Search Results (1,073)

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

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

Healthcare data rapidly increases, and patients seek customized, effective healthcare services. Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. Unlike previous reviews that separately address AI or big data, this work synthesizes their convergence through real-world case studies, cross-domain ML applications, and a critical discussion on ethical integration in smart diagnostics. The review focuses on the role of big data analysis and ML towards better diagnosis, improved efficiency of operations, and individualized care for patients. It explores the principal challenges of data heterogeneity, privacy, computational complexity, and advanced methods such as federated learning (FL) and edge computing. Applications in real-world settings, such as disease prediction, medical imaging, drug discovery, and remote monitoring, illustrate how ML methods, such as deep learning (DL) and natural language processing (NLP), enhance clinical decision-making. A comparison of ML models highlights their value in dealing with large and heterogeneous healthcare datasets. In addition, the use of nascent technologies such as wearables and Internet of Medical Things (IoMT) is examined for their role in supporting real-time data-driven delivery of healthcare. The paper emphasizes the pragmatic application of intelligent systems by highlighting case studies that reflect up to 95% diagnostic accuracy and cost savings. The review ends with future directions that seek to develop scalable, ethical, and interpretable AI-powered healthcare systems. It bridges the gap between ML algorithms and smart diagnostics, offering critical perspectives for clinicians, data scientists, and policymakers. Full article

Background: Overtime adversely affects physical and mental health, contributing to irritability, anxiety, reduced sleep, and even cardiovascular issues, ultimately lowering care quality and increasing turnover intentions. This study aimed to investigate whether prolonged overtime increases the risk of occupational burnout over time among healthcare workers. Methods: We conducted a four-year longitudinal observational study using secondary data from annual surveys (2021–2024) of healthcare workers at a medical university hospital in Taichung, Taiwan. Burnout was assessed using the personal burnout (PB) scale from the Copenhagen Burnout Inventory (CBI), with high PB levels (HPBL) defined as scores in the upper quartile of the 2021 baseline. Survival analysis utilizing the Kaplan–Meier method and Cox regression investigated burnout progression and the effects of overtime. Results: HPBL was defined as PB scores ≥45.83 (upper quartile in 2021). The proportions of HPBL were 30.28% (2021), 33.29% (2022), 36.75% (2023), and 32.51% (2024). Survival analysis confirmed that the risk of burnout increased over time, with the survival time estimated at 2.50 ± 0.03 years and lower survival probabilities observed among participants working overtime (Log-rank test, p < 0.0001). Multivariate logistics revealed overtime work, female gender, being a physician/nurse, and reduced sleep as independent risk factors for HPBL (OR = 3.14 for overtime, p < 0.001). These findings support the hypotheses on burnout progression and the impact of overtime. Conclusions: Overtime significantly heightens the risk of burnout, which worsens over time. Female sex, healthcare roles, obesity, and insufficient sleep are additional risk factors. Limiting overtime and proactive interventions are crucial to preventing burnout in healthcare workers. Full article

The increasing complexity and scale of electronic health records (EHRs) demand advanced tools for efficient data retrieval, summarization, and comparative analysis in clinical practice. MERA (Medical Electronic Records Assistant) is a Retrieval-Augmented Generation (RAG)-based AI system that addresses these needs by integrating domain-specific retrieval with large language models (LLMs) to deliver robust question answering, similarity search, and report summarization functionalities. MERA is designed to overcome key limitations of conventional LLMs in healthcare, such as hallucinations, outdated knowledge, and limited explainability. To ensure both privacy compliance and model robustness, we constructed a large synthetic dataset using state-of-the-art LLMs, including Mistral v0.3, Qwen 2.5, and Llama 3, and further validated MERA on de-identified real-world EHRs from the MIMIC-IV-Note dataset. Comprehensive evaluation demonstrates MERA’s high accuracy in medical question answering (correctness: 0.91; relevance: 0.98; groundedness: 0.89; retrieval relevance: 0.92), strong summarization performance (ROUGE-1 F1-score: 0.70; Jaccard similarity: 0.73), and effective similarity search (METEOR: 0.7–1.0 across diagnoses), with consistent results on real EHRs. The similarity search module empowers clinicians to efficiently identify and compare analogous patient cases, supporting differential diagnosis and personalized treatment planning. By generating concise, contextually relevant, and explainable insights, MERA reduces clinician workload and enhances decision-making. To our knowledge, this is the first system to integrate clinical question answering, summarization, and similarity search within a unified RAG-based framework. Full article

One of the largest future applications of computer vision is in the healthcare industry. Computer vision tasks are generally implemented in diverse medical imaging scenarios, including detecting or classifying diseases, predicting potential disease progression, analyzing cancer data for advancing future research, and conducting genetic analysis for personalized medicine. However, a critical drawback of using Computer Vision (CV) approaches is their limited reliability and transparency. Clinicians and patients must comprehend the rationale behind predictions or results to ensure trust and ethical deployment in clinical settings. This demonstrates the adoption of the idea of Explainable Computer Vision (X-CV), which enhances vision-relative interpretability. Among various methodologies, attribution-based approaches are widely employed by researchers to explain medical imaging outputs by identifying influential features. This article solely aims to explore how attribution-based X-CV methods work in medical imaging, what they are good for in real-world use, and what their main limitations are. This study evaluates X-CV techniques by conducting a thorough review of relevant reports, peer-reviewed journals, and methodological approaches to obtain an adequate understanding of attribution-based approaches. It explores how these techniques tackle computational complexity issues, improve diagnostic accuracy and aid clinical decision-making processes. This article intends to present a path that generalizes the concept of trustworthiness towards AI-based healthcare solutions. Full article

The global rise in urban-related health issues poses significant challenges to public health, particularly in cities facing socio-economic crises. In Lebanon, 70% of the population is experiencing financial hardship, and healthcare costs have surged by 172%, exacerbating the strain on medical services. Given these conditions, improving the quality and accessibility of green spaces offers a promising avenue for alleviating mental health issues in urban areas. This study investigates the psychological impact of nine urban public spaces in Beirut through a comprehensive survey methodology, involving 297 participants (locals and tourists) who rated these spaces using Likert-scale measures. The findings reveal location-specific barriers, with Saanayeh Park rated highest in quality and Martyr’s Square rated lowest. The analysis identifies facility quality as the most significant factor influencing space quality, contributing 73.6% to the overall assessment, while activity factors have a lesser impact. The study further highlights a moderate positive association (Spearman’s rho = 0.30) between public space quality and mental well-being in Beirut. This study employs a hybrid methodology combining Research for Design (RfD) and Research Through Designing (RTD). Empirical data informed spatial strategies, while iterative design served as a tool for generating context-specific knowledge. Design enhancements—such as sensory plantings, shading systems, and social nodes—aim to improve well-being through better public space quality. The proposed interventions support mental health, life satisfaction, climate resilience, and urban inclusivity. The findings offer actionable insights for cities facing public health and spatial equity challenges in crisis contexts. Full article

Introduction: Adolescents and young adults (AYAs), especially those from underserved communities, often face barriers to sexual and reproductive health (SRH). This pilot study evaluated the implementation of mobile health technologies to promote SRH care, including the integration of the Rapid Adolescent Prevention Screening^TM (RAAPS) and the Health-E You/Salud iTu^TM (Health-E You) app at a School-Based Health Center (SBHC) in Los Angeles using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework. Methods: This multi-method pilot study included the implementation of an integrated tool with two components, the RAAPS electronic health screening tool and the Health-E You app, which delivers tailored SRH education and contraceptive decision support to patients (who were sex-assigned as female at birth) and provides an electronic summary to clinicians to better prepare them for the visit with their patient. Quantitative data on tool usage were collected directly from the back-end data storage for the apps, and qualitative data were obtained through semi-structured interviews and in-clinic observations. Thematic analysis was conducted to identify implementation barriers and facilitators. Results: Between April 2024 and June 2024, 60 unique patients (14–19 years of age) had a healthcare visit. Of these, 35.00% used the integrated RAAPS/Health-E You app, and 88.33% completed the Health-E You app only. All five clinic staff were interviewed and expressed that they valued the tools for their educational impact, noting that they enhanced SRH discussions and helped uncover sensitive information that students might not disclose face-to-face. However, the tools affected clinic workflows and caused rooming delays due to the time-intensive setup process and lack of integration with the clinic’s primary electronic medical record system. In addition, they also reported that the time to complete the screener and app within the context of a 30-min appointment limited the time available for direct patient care. Additionally, staff reported that some students struggled with the two-step process and did not complete all components of the tool. Despite these challenges, clinic staff strongly supported renewing the RAAPS license and continued use of the Health-E You app, emphasizing the platform’s potential for improving SRH care and its educational value. Conclusions: The integrated RAAPS and Health-E You app platform demonstrated educational value and improved SRH care but faced operational and technical barriers in implementing the tool. These findings emphasize the potential of such tools to address SRH disparities among vulnerable AYAs while providing a framework for future implementations in SBHCs. 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

Background/Objective(s): Post-discharge clinical needs of extremely preterm (EP) infants are not well defined. The aim of this study is to evaluate healthcare utilization after discharge in infants born EP and compare it to the general pediatric population. Methods: This study involved a post hoc analysis of infants born 24-0/7 to 27-6/7 weeks’ gestation enrolled in the Preterm Erythropoietin Neuroprotection (PENUT) Trial who had at least one follow-up survey representing their course between 24 and 60 months of age. The results were compared to the general population data from the Kids’ Inpatient Database, Nationwide Emergency Department Sample, and National Health and Nutrition Examination Survey. Results: Maternal, infant, and hospitalization characteristics for PENUT infants who survived to discharge (n = 828) compared to those with follow-up (n = 569) were similar except for race and maternal age. Overall, EP infants had an overall lower rate of ED visits (31% vs. 68%) but a higher rate of hospitalizations (11% vs. 3%). EP infants were less likely to go to the ED for gastrointestinal (5% vs. 12%) and dermatologic (1% vs. 6%) concerns but more likely to go to the ED for procedures (7% vs. <1%). EP infants had a higher rate of medication use (56% vs. 14%) in all categories except psychiatric medications. Conclusions: While EP infants had higher rates of specialty healthcare utilization relative to the general pediatric population, they were less likely to visit the ED overall, particularly for common concerns in this age range. This may reflect improved access and navigation of the healthcare system by EP caregivers. Full article

Smart healthcare establishes a safe, reliable, and efficient medical information system for the public with the help of the Internet of Things, cloud storage, and other Internet technologies. To enable secure data sharing and case-matching functions in smart healthcare, we construct a revocable identity-based matchmaking encryption with an equality test (RIBME-ET) scheme for smart healthcare. Our scheme not only ensures the confidentiality and authenticity of messages and protects the privacy of users, but also enables a cloud server to perform equality tests on encrypted ciphertexts from different identities to determine whether they contain the same plaintext and protects the confidentiality of data in the system through a user revocation mechanism. Compared with the existing identity-based encryption with equality test (IBEET) and identity-based matchmaking encryption with equality test (IBME-ET) schemes, we have improved the efficiency of the scheme and reduced communication overhead. In addition, the scheme’s security is proven in the random oracle model under the computational bilinear Diffie–Hellman (CBDH) assumption. Finally, the feasibility and effectiveness of the proposed scheme are verified by performance analysis. 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: Tobacco use is disproportionately high in rural areas, contributing to elevated cancer mortality, yet it often goes untreated due to limited access to care, high poverty and uninsured rates, and co-occurring substance use disorders (SUDs). This study explored the utility of using rapid qualitative analysis (RQA) to guide the adaptation of a tobacco-free workplace program (TFWP) in Texas healthcare centers serving adults with SUDs in medically underserved areas. Methods: From September–December 2023 and May–July 2024, we conducted 11 pre-implementation, virtual semi-structured group interviews focused on adapting the TFWP to local contexts (N = 69); 7 with providers (n = 34) and managers (n = 12) and 4 with patients (n = 23) in 6 healthcare centers. Two qualified analysts independently summarized transcripts, using RQA templates of key domains drawn from interview guides to summarize and organize data in matrices, enabling systematic comparison. Results: The main themes identified were minimal organizational tobacco cessation support and practices, and attitudinal barriers, as follows: (1) the need for program materials tailored to local populations; (2) limited tobacco cessation practices and partial policies—staff requested guidance on enhancing tobacco screenings and cessation delivery, and integrating new interventions; (3) contradictory views on treating tobacco use that can inhibit implementation (e.g., wanting to quit yet anxious that quitting would cause SUD relapse); and (4) inadequate environmental supports—staff requested treating tobacco-use training, patients group cessation counseling; both requested nicotine replacement therapy. Conclusions: RQA identified key areas requiring capacity development through participants’ willingness to adopt the following adaptations: program content (e.g., trainings and tailored educational materials), delivery methods/systems (e.g., adopting additional tobacco care interventions) and implementation strategies (e.g., integrating tobacco cessation practices into routine care) critical to optimizing TFWP fit and implementation. The study findings can inform timely formative evaluation processes to design and tailor similar intervention efforts by addressing site-specific needs and implementation barriers to enhance program uptake. Full article

This study investigates the accessibility of public healthcare services in Olsztyn County, a major urban center in the Warmia and Mazury region of Poland. The aim was to develop a methodological framework using Geographic Information System (GIS) tools and spatial data to assess the local availability of healthcare infrastructure. The analysis included key facilities such as hospitals, clinics, pharmacies, and specialized outpatient services. A spatial accessibility indicator was constructed to evaluate and compare access levels across municipalities. The results show a clear disparity between urban and rural areas, with significantly better access in cities. Several rural municipalities were found to have limited or no access to essential healthcare services. These findings highlight the uneven spatial distribution of medical infrastructure and point to the need for targeted strategies to improve service availability in underserved areas. The proposed methodological approach may support future studies and inform local and regional planning aimed at reducing healthcare inequalities and improving access for all residents, regardless of their location. This research contributes to the growing body of evidence emphasizing the role of spatial analysis in assessing public service accessibility and supports the development of more equitable healthcare systems at the local level. Full article

Mid-infrared passive spectroscopic imaging is a novel non-invasive and remote sensing method based on Planck’s law. It enables the acquisition of component-specific information from the human body by measuring naturally emitted thermal radiation in the mid-infrared region. Unlike active methods that require an external light source, our passive approach harnesses the body’s own emission, thereby enabling safe, long-term monitoring. In this study, we successfully demonstrated the simultaneous, non-invasive measurements of blood glucose and lactate levels of the human body using this method. The measurements, conducted over approximately 80 min, provided emittance data derived from mid-infrared passive spectroscopy that showed a temporal correlation with values obtained using conventional blood collection sensors. Furthermore, to evaluate localized metabolic changes, we performed k-means clustering analysis of the spectral data obtained from the upper arm. This enabled visualization of time-dependent lactate responses with spatial resolution. These results demonstrate the feasibility of multi-component monitoring without physical contact or biological sampling. The proposed technique holds promise for translation to medical diagnostics, continuous health monitoring, and sports medicine, in addition to facilitating the development of next-generation healthcare technologies. Full article