Search Results (339)

CT scanners are essential tools in modern medical imaging. Sudden failures of their X-ray tubes can lead to equipment downtime, affecting healthcare services and patient diagnosis. However, existing prediction methods based on a single model struggle to adapt to the multi-stage variation characteristics of tube lifespan and have limited modeling capabilities for temporal features. To address these issues, this paper proposes an intelligent prediction architecture for CT tubes’ remaining useful life based on a dual-branch neural network. This architecture consists of two specialized branches: a residual self-attention BiLSTM (RSA-BiLSTM) and a multi-layer dilation temporal convolutional network (D-TCN). The RSA-BiLSTM branch extracts multi-scale features and also enhances the long-term dependency modeling capability for temporal data. The D-TCN branch captures multi-scale temporal features through multi-layer dilated convolutions, effectively handling non-linear changes in the degradation phase. Furthermore, a dynamic phase detector is applied to integrate the prediction results from both branches. In terms of optimization strategy, a dynamically weighted triplet mixed loss function is designed to adjust the weight ratios of different prediction tasks, effectively solving the problems of sample imbalance and uneven prediction accuracy. Experimental results using leave-one-out cross-validation (LOOCV) on six different CT tube datasets show that the proposed method achieved significant advantages over five comparison models, with an average MSE of 2.92, MAE of 0.46, and R² of 0.77. The LOOCV strategy ensures robust evaluation by testing each tube dataset independently while training on the remaining five, providing reliable generalization assessment across different CT equipment. Ablation experiments further confirmed that the collaborative design of multiple components is significant for improving the accuracy of X-ray tubes remaining life prediction. Full article

Background: The education process for newly diagnosed Type 1 diabetes mellitus (T1D) patients and their families, primarily led by diabetes specialist nurses, is essential for gaining knowledge about the disease and its management. However, few assessment tools have been employed to evaluate long-term knowledge retention among T1D patients years after diagnosis. Methods: We developed a 20-question test to assess the knowledge of patients and their families at the conclusion of the initial education process and again 6–12 months later. Demographic and clinical data were also collected. Statistical analyses included comparisons between the first and second test results, as well as evaluation of potential contributing factors. The internal consistency and construct validity of the questionnaire were evaluated. Results: Forty-four patients completed both assessments, with a median interval of 11.5 months between them. The average score on the first test was 88.6, which declined to 82.7 on the second assessment (p < 0.001). In univariate analysis, factors positively associated with higher scores included Jewish ethnicity, lower HbA1c levels, and shorter hospitalization duration. Multivariate analysis revealed that parents had lower odds of experiencing a significant score decline compared to patients. Cronbach’s alpha was 0.69, and Principal Component Analysis (PCA) identified eight components accounting for 67.1% of the total variance. Conclusions: Healthcare providers should consider offering re-education to patients and their families approximately one year after diagnosis, with particular attention to high-risk populations during the initial education phase. Further studies are needed to examine this tool’s performance in larger cohorts. Full article

In recent years, the Internet of Things (IoT) has experienced considerable developments and has played an important role in various domains such as industry, agriculture, healthcare, transportation, and environment, especially for smart cities. Along with that, wireless sensor networks (WSNs) are considered to be important components of the IoT system (WSN-IoT) to create smart applications and automate processes. As the number of connected IoT devices increases, privacy and security issues become more complicated due to their external working environments and limited resources. Hence, solutions need to be updated to ensure that data and user privacy are protected from threats and attacks. To support the safety and reliability of such systems, in this paper, security issues in the WSN-IoT are addressed and classified as identifying security challenges and requirements for different kinds of attacks in either WSNs or IoT systems. In addition, security solutions corresponding to different types of attacks are provided, analyzed, and evaluated. We provide different comparisons and classifications based on specific goals and applications that hopefully can suggest suitable solutions for specific purposes in practical. We also suggest some research directions to support new security mechanisms. Full article

Robotic search of people in human-centered environments, including healthcare settings, is challenging, as autonomous robots need to locate people without complete or any prior knowledge of their schedules, plans, or locations. Furthermore, robots need to be able to adapt to real-time events that can influence a person’s plan in an environment. In this paper, we present MLLM-Search, a novel zero-shot person search architecture that leverages multimodal large language models (MLLM) to address the mobile robot problem of searching for a person under event-driven scenarios with varying user schedules. Our approach introduces a novel visual prompting method to provide robots with spatial understanding of the environment by generating a spatially grounded waypoint map, representing navigable waypoints using a topological graph and regions by semantic labels. This is incorporated into an MLLM with a region planner that selects the next search region based on the semantic relevance to the search scenario and a waypoint planner that generates a search path by considering the semantically relevant objects and the local spatial context through our unique spatial chain-of-thought prompting approach. Extensive 3D photorealistic experiments were conducted to validate the performance of MLLM-Search in searching for a person with a changing schedule in different environments. An ablation study was also conducted to validate the main design choices of MLLM-Search. Furthermore, a comparison study with state-of-the-art search methods demonstrated that MLLM-Search outperforms existing methods with respect to search efficiency. Real-world experiments with a mobile robot in a multi-room floor of a building showed that MLLM-Search was able to generalize to new and unseen environments. Full article

Deep learning (DL) continues to play a pivotal role in a wide range of intelligent systems, including autonomous machines, smart surveillance, industrial automation, and portable healthcare technologies. These applications often demand low-latency inference and efficient resource utilization, especially when deployed on embedded or edge devices with limited computational capacity. As DL models become increasingly complex, selecting the right inference framework is essential to meeting performance and deployment goals. In this work, we conduct a comprehensive comparison of five widely adopted inference frameworks: PyTorch, ONNX Runtime, TensorRT, Apache TVM, and JAX. All experiments are performed on the NVIDIA Jetson AGX Orin platform, a high-performance computing solution tailored for edge artificial intelligence workloads. The evaluation considers several key performance metrics, including inference accuracy, inference time, throughput, memory usage, and power consumption. Each framework is tested using a wide range of convolutional and transformer models and analyzed in terms of deployment complexity, runtime efficiency, and hardware utilization. Our results show that certain frameworks offer superior inference speed and throughput, while others provide advantages in flexibility, portability, or ease of integration. We also observe meaningful differences in how each framework manages system memory and power under various load conditions. This study offers practical insights into the trade-offs associated with deploying DL inference on resource-constrained hardware. 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

Background/Objectives: Clostridioides difficile infection (CDI) is a major cause of infectious diarrhea in the inpatient and community setting. Real-world data outside the strict environment of randomized controlled trials (RCTs) are needed to improve the quality of evidence. The aim of this study was to compare different clinical outcomes of CDI patients treated with fidaxomicin with those treated with vancomycin using a representative patient population in the United States of America (USA). Methods: Comprehensive real-world data were analyzed for this retrospective observational study, provided by the TriNetX database, an international research network with electronic health records from multiple USA healthcare providers. This includes in- and outpatients treated with fidaxomicin (FDX) or vancomycin (VAN) for CDI between 01/2013 and 12/2023. The following cohorts were compared: (i) patients treated with fidaxomicin within 10 days following CDI diagnosis (FDX group) vs. (ii) patients treated with vancomycin within 10 days following CDI diagnosis (VAN group). Outcomes analysis between the two cohorts was performed after propensity score matching and included event risk and Kaplan–Meier survival analyses for the following concomitant diseases/events occurring during an observational period of 12 months following CDI diagnosis: death, sepsis, candidiasis, infections caused by vancomycin-resistant enterococci, inflammatory bowel disease, cardiovascular disease, psychological disease, central line-associated blood stream infection, surgical site infection, and ventilator-associated pneumonia. Results: Following propensity score matching, 2170 patients were included in the FDX group and VAN groups, respectively. The event risk analysis demonstrated improved outcomes of patients treated with FDX compared to VAN in 6 out of the 10 events that were analyzed. The highest risk ratio (RR) and odds ratio (OR) were found for sepsis (RR: 3.409; OR: 3.635), candidiasis (RR: 2.347; OR: 2.431), and death (RR: 1.710; OR: 1.811). The Kaplan–Meier survival analysis showed an overall survival rate until the end of the 12-month observational period of 87.06% in the FDX group and 78.49% in the VAN group (log-rank p < 0.001). Conclusions: Our comparative event risk analysis demonstrated improved outcomes for patients treated with FDX compared to VAN in most of the observed events and underlines the results of previously conducted RCTs, highlighting the beneficial role of FDX compared to VAN. Further big data analyses from other industrialized countries are needed for comparison with our observations. Full article

With the continuous advancement of optical fiber micromachining technology, C-type optical fibers have demonstrated significant potential in the field of optical fiber sensing. By partially or completely removing specific regions of the cladding, a “leakage window” is created, enabling interaction between the optical field and external substances. This structure has facilitated the development of various sensors. This paper reviews recent progress in the research and applications of C-type optical fibers in optical sensing. Based on sensing principles and application scenarios, C-type optical fiber sensors can be categorized into two main types: interferometric and photonic crystal types. This article discusses the fundamental operating principles and structural characteristics of each type, and provides a detailed comparison of their respective advantages and disadvantages. Studies have shown that sensors based on C-type fiber structures offer notable benefits such as simple fabrication, excellent mechanical performance, strong anti-interference capability, and high sensitivity. Therefore, they hold great promise for applications in intelligent monitoring, environmental detection, and healthcare. Finally, this review outlines future research directions for C-type fiber sensors. As technology continues to evolve, future studies are expected to focus on improving sensor stability, expanding application scenarios, and addressing challenges in current fabrication techniques. Full article

Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a novel synthesis by unifying recent innovations across multiple diagnostic imaging modalities, such as CT, MRI, and ultrasound, with emerging biochemical, genetic, and digital technologies. While existing reviews typically focus on advances within a single modality or for specific MSK conditions, this paper integrates a broad spectrum of developments to highlight how use of multimodal diagnostic strategies in combination can improve disease detection, stratification, and clinical decision-making in real-world settings. Technological developments in imaging, including photon-counting detector computed tomography, quantitative magnetic resonance imaging, and four-dimensional computed tomography, have enhanced the ability to visualize structural and dynamic musculoskeletal abnormalities with greater precision. Molecular imaging and biochemical markers such as CTX-II (C-terminal cross-linked telopeptides of type II collagen) and PINP (procollagen type I N-propeptide) provide early, objective indicators of tissue degeneration and bone turnover, while genetic and epigenetic profiling can elucidate individual patterns of susceptibility. Point-of-care ultrasound and portable diagnostic devices have expanded real-time imaging and functional assessment capabilities across diverse clinical settings. Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. Wearable sensors and mobile health technologies extend continuous monitoring beyond traditional healthcare environments, generating real-world data critical for dynamic disease management. However, standardization of diagnostic protocols, rigorous validation of novel methodologies, and thoughtful integration of multimodal data remain essential for translating technological advances into improved patient outcomes. Despite these advances, several key limitations constrain widespread clinical adoption. Imaging modalities lack standardized acquisition protocols and reference values, making cross-site comparison and clinical interpretation difficult. AI-driven diagnostic tools often suffer from limited external validation and transparency (“black-box” models), impacting clinicians’ trust and hindering regulatory approval. Molecular markers like CTX-II and PINP, though promising, show variability due to diurnal fluctuations and comorbid conditions, complicating their use in routine monitoring. Integration of multimodal data, especially across imaging, omics, and wearable devices, remains technically and logistically complex, requiring robust data infrastructure and informatics expertise not yet widely available in MSK clinical practice. Furthermore, reimbursement models have not caught up with many of these innovations, limiting access in resource-constrained healthcare settings. As these fields converge, musculoskeletal diagnostics methods are poised to evolve into a more precise, personalized, and patient-centered discipline, driving meaningful improvements in musculoskeletal health worldwide. Full article

Sleep position recognition plays a crucial role in diagnosing and managing various health conditions, such as sleep apnea, pressure ulcers, and musculoskeletal disorders. Accurate monitoring of body posture during sleep can provide valuable insights for clinicians and support the development of intelligent healthcare systems. This research presents a comparative analysis of sleep position recognition using two distinct approaches: image-based deep learning and accelerometer-based classification. There are five classes: prone, supine, right side, left side, and wake up. For the image-based method, the Visual Geometry Group 16 (VGG16) convolutional neural network was fine-tuned with data augmentation strategies including rotation, reflection, scaling, and translation to enhance model generalization. The image-based model achieved an overall accuracy of 93.49%, with perfect precision and recall for “right side” and “wakeup” positions, but slightly lower performance for “left side” and “supine” classes. In contrast, the accelerometer-based method employed a feedforward neural network trained on features extracted from segmented accelerometer data, such as signal sum, standard deviation, maximum, and spike count. This method yielded superior performance, reaching an accuracy exceeding 99.8% across most sleep positions. The “wake up” position was particularly easy to detect due to the absence of body movements such as heartbeat or respiration when the person is no longer in bed. The results demonstrate that while image-based models are effective, accelerometer-based classification offers higher precision and robustness, particularly in real-time and privacy-sensitive scenarios. Further comparisons of the system characteristics, data size, and training time are also carried out to offer crucial insights for selecting the appropriate technology in clinical, in-home, or embedded healthcare monitoring applications. Full article

Background and Objectives: Traditionally, we evaluate the size of a wound by using Opsite Flexigrid transparent film dressing, placing it over the wound, tracing the edges of the wound, and then calculating the area. However, this method is both time-consuming and subjective, often leading to varying results depending on the individual performing the assessment. In this study, our goal is to provide an objective method to calculate the wound size and solve variations in photo-taking distance caused by different medical practitioners or at different times, as these can lead to inaccurate wound size assessments. To evaluate this, we employed K-means clustering and used a QR code as a reference to analyze images of the same wound captured at varying distances, objectively quantifying the areas of 40 wounds. This study aims to develop an objective method for calculating the wound size, addressing variations in photo-taking distance that occur across different medical personnel or time points—factors that can compromise measurement accuracy. By improving consistency and reducing the manual workload, this approach also seeks to enhance the efficiency of healthcare providers. We applied K-means clustering for wound segmentation and used a QR code as a spatial reference. Images of the same wounds taken at varying distances were analyzed, and the wound areas of 40 cases were objectively quantified. Materials and Methods: We employed K-means clustering and used a QR code as a reference to analyze wound photos taken by different medical practitioners in the outpatient consulting room. K-means clustering is a machine learning algorithm that segments the wound region by grouping pixels in an image according to their color similarity. It organizes data points into clusters based on shared features. Based on this algorithm, we can use it to identify the wound region and determine its pixel area. We also used a QR code as a reference because of its unique graphical pattern. We used the printed QR code on the patient’s identification sticker as a reference for length. By calculating the ratio of the number of pixels within the square area of the QR code to its actual area, we applied this ratio to the detected wound pixel area, enabling us to calculate the wound’s actual size. The printed patient identification stickers were all uniform in size and format, allowing us to apply this method consistently to every patient. Results: The results support the accuracy of our algorithm when tested on a standard one-cent coin. The paired t-test comparing the first and second photos shot yielded a p-value of 0.370, indicating no significant difference between the two. Similarly, the t-test comparing the first and third photos shot produced a p-value of 0.179, also showing no significant difference. The comparison between the second and third photos shot resulted in a p-value of 0.547, again indicating no significant difference. Since all p-values are greater than 0.05, none of the test pairs show statistically significant differences. These findings suggest that the three randomly taken photo shots produce consistent results and can be considered equivalent. Conclusions: Our algorithm for wound area assessment is highly reliable, interchangeable, and consistently produces accurate results. This objective and practical method can aid clinical decision-making by tracking wound progression over time. Full article

Background/Objectives: This research presents a novel analytical method for breast tumor characterization and tissue classification by leveraging intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) combined with hyperspectral imaging techniques and deep learning. Traditionally, dynamic contrast-enhanced MRI (DCE-MRI) is employed for breast tumor diagnosis, but it involves gadolinium-based contrast agents, which carry potential health risks. IVIM imaging extends conventional diffusion-weighted imaging (DWI) by explicitly separating the signal decay into components representing true molecular diffusion ($D$) and microcirculation of capillary blood (pseudo-diffusion or $D*$). This separation allows for a more comprehensive, non-invasive assessment of tissue characteristics without the need for contrast agents, thereby offering a safer alternative for breast cancer diagnosis. The primary purpose of this study was to evaluate different methods for breast tumor characterization using IVIM-DWI data treated as hyperspectral image stacks. Dice similarity coefficients and Jaccard indices were specifically used to evaluate the spatial segmentation accuracy of tumor boundaries, confirmed by experienced physicians on dynamic contrast-enhanced MRI (DCE-MRI), emphasizing detailed tumor characterization rather than binary diagnosis of cancer. Methods: The data source for this study consisted of breast MRI scans obtained from 22 patients diagnosed with mass-type breast cancer, resulting in 22 distinct mass tumor cases analyzed. MR images were acquired using a 3T MRI system (Discovery MR750 3.0 Tesla, GE Healthcare, Chicago, IL, USA) with axial IVIM sequences and a bipolar pulsed gradient spin echo sequence. Multiple b-values ranging from 0 to 2500 s/mm² were utilized, specifically thirteen original b-values (0, 15, 30, 45, 60, 100, 200, 400, 600, 1000, 1500, 2000, and 2500 s/mm²), with the last four b-value images replicated once for a total of 17 bands used in the analysis. The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). The comparisons were assessed by evaluating the similarity of the detection results from each method to ground truth tumor areas, which were manually drawn on DCE-MRI images and confirmed by experienced physicians. Similarity was quantitatively measured using the Dice similarity coefficient and the Jaccard index. Additionally, the performance of the detectors was evaluated using 3D-ROC analysis and its derived criteria (AUC_OD, AUC_TD, AUC_BS, AUC_TD−BS, AUC_ODP, AUC_SNPR). Results: The findings objectively demonstrated that the DNN method achieved superior performance in breast tumor detection compared to KCEM and I-KCEM. Specifically, the DNN yielded a Dice similarity coefficient of 86.56% and a Jaccard index of 76.30%, whereas KCEM achieved 78.49% (Dice) and 64.60% (Jaccard), and I-KCEM achieved 78.55% (Dice) and 61.37% (Jaccard). Evaluation using 3D-ROC analysis also indicated that the DNN was the best detector based on metrics like target detection rate and overall effectiveness. The DNN model further exhibited the capability to identify tumor heterogeneity, differentiating high- and low-cellularity regions. Quantitative parameters, including apparent diffusion coefficient ($ADC$), pure diffusion coefficient ($D$), pseudo-diffusion coefficient ($D*$), and perfusion fraction ($PF$), were calculated and analyzed, providing insights into the diffusion characteristics of different breast tissues. Analysis of signal intensity decay curves generated from these parameters further illustrated distinct diffusion patterns and confirmed that high cellularity tumor regions showed greater water molecule confinement compared to low cellularity regions. Conclusions: This study highlights the potential of combining IVIM-DWI, hyperspectral imaging techniques, and deep learning as a robust, safe, and effective non-invasive diagnostic tool for breast cancer, offering a valuable alternative to contrast-enhanced methods by providing detailed information about tissue microstructure and heterogeneity without the need for contrast agents. Full article

Background/Objectives: Large-language modules facilitate accessing health information instantaneously. However, they do not provide the same level of accuracy or detail. In pediatric orthopedics, where parents have urgent concerns regarding knee deformities (bowlegs and knock knees), the accuracy and dependability of these chatbots can affect parent decisions to seek treatment. The goal of this study was to analyze how AI chatbots addressed parental concerns regarding pediatric knee deformities. Methods: A set of twenty standardized questions, consisting of ten questions each on bowlegs and knock knees, were designed through literature reviews and through analysis of parental discussion forums and expert consultations. Each of the three chatbots (ChatGPT, Gemini, and Copilot) was asked the same set of questions. Five pediatric orthopedic surgeons were then asked to rate each response for accuracy, clarity, and comprehensiveness, along with the degree of misleading information provided, on a scale of 1–5. The reliability among raters was calculated using intraclass correlation coefficients (ICCs), while differences among the chatbots were assessed using a Kruskal–Wallis test with post hoc pairwise comparisons. Results: All three chatbots displayed a moderate-to-good score for inter-rater reliability. ChatGPT and Gemini’s scores were higher for accuracy and comprehensiveness than Copilot’s (p < 0.05). However, no notable differences were found in clarity or in the likelihood of giving incorrect answers. Overall, more detailed and precise responses were given by ChatGPT and Gemini, while, with regard to clarity, Copilot performed comparably but was less thorough. Conclusions: There were notable discrepancies in performance across the AI chatbots in providing pediatric orthopedic information, which demonstrates indications of evolving potential. In comparison to Copilot, ChatGPT and Gemini were relatively more accurate and comprehensive. These results highlight the persistent requirement for real-time supervision and stringent validation when employing chatbots in the context of pediatric healthcare. Full article

Engaging patients in medication safety is essential but remains under-addressed in hospital settings. This pilot study aimed to assess the impact of an educational intervention—the Medication Safety Adventure Trail—on medication safety knowledge and satisfaction among hospital visitors. A quasi-experimental pre-post intervention using this educational tool was conducted over five days. A booth was set up in a hospital lobby inviting all passers-by to follow a six-step trail involving riddles to solve. The experiment comprised three phases: 1. Briefing plus pre-test; 2. The trail; 3. Debriefing plus post-test. A logistic mixed-effects model was employed to assess changes in the odds of correct responses to eight items between the pre-test and post-test. A five-point scale assessed participants’ degrees of certainty (DC) in their answers, and a comparison pre- and post-test was performed with a linear mixed-effects model. Satisfaction was based on Kirkpatrick’s levels 1 and 2 (reaction and learning) and was assessed using categorical scales and open-ended questions. A total of 93 participants completed the trail (60% non-healthcare professionals, 36% healthcare professionals, and 4% unspecified). The odds of a correct answer were higher at post-test than at pre-test (72% vs. 51%, p < 0.001), and the odds of providing a correct answer were nearly five times higher following the activity compared to before (OR = 4.8 [95%CI 3.5 to 6.4], p < 0.001). The mean DC was also improved from pre-test to post-test (4.43, 95%CI [4.36–4.49] vs. 4.83, 95%CI [4.80–4.86]; p < 0.001). All 93 participants reported being either very satisfied (89%) or satisfied (11%) with the educational tool. The tool significantly improved participants’ knowledge about medication safety issues and was appreciated. Full article

Background: Tobacco use is responsible for eight million preventable deaths annually, making it a major modifiable risk factor for chronic conditions such as cardiovascular diseases, respiratory illnesses, and over 20 types of cancers. Objective: This study aimed to systematically review the barriers and facilitators of tobacco cessation interventions at both the population and healthcare system levels in the U.S. Understanding these determinants is critical for narrowing health disparities, optimizing resource allocation, and ultimately, enhancing tobacco cessation success rates across all demographic groups. Methods: A comprehensive literature search was conducted across the PubMed, Embase, and Web of Science databases, guided by the population, intervention, comparison, and outcome framework and quality assessment guided by PRISMA guidelines. Data extraction focused on study characteristics, intervention types, barriers, facilitators, and cessation outcomes at both the population and health system levels. The random effects forest plots were graphed to estimate pooled effect sizes for both medical and non-medical interventions. Results: A total of 35 studies met the inclusion criteria from an initial pool of 1555 identified records. Socioeconomic disadvantages, digital inequities, and low motivation constitute primary barriers at the individual level, while systemic factors such as healthcare access limitations, inadequate provider engagement, and lack of financial support further hinder cessation efforts. Financial incentives, culturally tailored interventions, and digital engagement strategies significantly improve tobacco cessation outcomes. Public health implications: as identified by the study, tailored interventions, the expansion of health coverage policies to include intervention, digital solutions, and healthcare resource workforce training will help improve tobacco cessation intervention outcomes. Full article