Search Results (3,700)

While artificial intelligence (AI) is expected to drive paradigm-shifting transformations, many initiatives result in merely incremental optimization. Anchored in strategic human capital theory, this study shifts the analytical focus from the scale of elite technical talent, conceptualized as AI stars, to the configuration of their knowledge structures to unpack this paradox. Using a dataset of 1270 medical AI patents from corporate R&D teams, we employed high-dimensional fixed-effects models to examine these dynamics. The results reveal that while the knowledge diversity of AI stars acts as a potent engine for breakthrough innovation, this generative capacity is attenuated by excessive domain familiarity. Specifically, direct domain familiarity (derived from internal experience) and indirect domain familiarity (absorbed through external collaborative networks) negatively moderate this relationship, a dynamic theorized to operate through internal cognitive entrenchment and external relational conformity, respectively. Extending the efficiency-driven consensus regarding bilingual expertise, these findings demonstrate that excessive domain embeddedness transforms from an informational bridge into a restrictive constraint during paradigm-shifting innovations, particularly within highly institutionalized environments. Full article

The dissimilar combination of NiTi shape memory alloy and Ti6Al4V titanium alloy offers superelasticity, biocompatibility and high specific strength, showing broad application prospects in aerospace and medical fields. However, when fabricating NiTi/Ti6Al4V composite components by multi-material laser powder bed fusion (MM-LPBF), brittle cracks or even complete delamination easily occur at the interface. In this paper, without relying on intermediate interlayer materials, we innovatively propose a root-inspired three-dimensional bio-interlocking interface structure. By means of macroscopic three-dimensional geometric interlocking, the crack propagation path and load transfer mode are forced to change. Using the branching angle (45°, 60°) and the structural size multiplier (1.2, 1.5) as variables, the influence of the bio-inspired geometric parameters on the interfacial forming quality, microstructure and mechanical properties was systematically investigated. The results show that the branching angle is the primary factor determining the performance. The 45° low-angle branched specimens exhibit overall brittle delamination along the flat metallurgical reaction interface under shear loading, with an average shear strength of only 17.47 MPa. In contrast, the 60° high-angle branched specimens, owing to their larger normal embedding depth, exhibit a failure mode transitioning to a mixed mode that includes crack deflection, branch shearing and plastic tearing of the Ti6Al4V matrix. Although TEM confirms that a continuous Ti₂Ni brittle phase still exists at the interface, the optimised 60–1.5 structure increases the average shear strength to 128.37 MPa, which is more than six times higher than that of the 45–1.2 group (17.47 MPa). This “geometrical constraint toughening” strategy provides a new paradigm for the interfacial strengthening of dissimilar metals without relying on metallurgical modification. Full article

Background: Facial nerve regeneration across segmental defects remains challenging despite advances in nerve guidance conduits. Tissue engineering strategies that combine biomaterial scaffolds with bioactive and cellular components may enhance functional and structural nerve repair. This study investigated the regenerative efficacy of a cellulose-based nerve conduit augmented with Matrigel, mesenchymal stem cells (MSCs), and placental extract (PE) in a rat facial nerve gap model. Methods: A 2 mm defect was created at the main trunk of the facial nerve in adult Sprague–Dawley rats. Animals were divided into three groups: control group (Cellulose/Matrigel), group I (Cellulose/Matrigel/MSC), and group II (Cellulose/Matrigel/MSC/PE). Functional recovery was assessed at 2, 4, 6, and 8 weeks postoperatively using slow-motion vibrissa movement analysis, electrically stimulated facial nerve action potential measurements, and facial nerve blood flow by laser Doppler blood flow analysis. Morphological regeneration was evaluated using light microscopy and transmission electron microscopy. Western blotting using facial muscle was performed. Results: All groups exhibited time-dependent facial nerve regeneration. However, group II demonstrated significantly enhanced functional recovery, greater electrophysiological responses, increased nerve blood flow, and superior histological and ultrastructural regeneration compared with the other groups. These improvements were particularly prominent at 6 and 8 weeks postoperatively. Conclusions: The combination of mesenchymal stem cells and PE within a cellulose-based nerve conduit synergistically enhanced facial nerve regeneration. This tissue-engineered strategy may represent an effective approach for improving peripheral nerve repair. Full article

The aorta is a single arterial organ, yet the clinical response to its diseases is fragmented across specialties, institutions, and nations. Acute aortic syndromes remain lethal at every stage of the care continuum: a large share of patients with type A dissection die before reaching any hospital, roughly half die before reaching a specialist centre, a substantial proportion are misdiagnosed at first medical contact, and untreated mortality has historically been estimated at approximately one to two percent per hour. Reported 30-day mortality differs across surgical registry cohorts—for example, 7.6% in the Japan Cardiovascular Surgery Database and 16.9% in the cited GERAADA cohort—but such ecological comparisons are descriptive and cannot identify the contribution of organisational, biological, clinical, or ascertainment differences. This review synthesises registry, population, and health-services data from Europe, Japan, North America, and low- and middle-income settings to characterise where the pathway fails and what each system has already demonstrated. We argue that the next material advance in aortic care will come not from any single innovation in isolation but from the architecture that connects prevention, diagnosis, treatment, and follow-up and that system redesign and technological progress are complementary rather than competing; we propose a five-pillar framework (Prevention, Presentation, Pathway, Person, and Learning) mapped throughout to published exemplars and future research priorities. Full article

Background: Despite more than 30 licenced antiseizure medications (ASMs), approximately one third of people with epilepsy remain drug-resistant, and developmental and epileptic encephalopathies represent one of the greatest unmet needs in epilepsy therapeutics. The past decade has produced a substantial reorientation of ASM discovery, driven by epilepsy genetics, new disease models, advances in drug screening, and innovative therapeutic modalities. Objective: The objective of this study was to review the contemporary clinical-stage pipeline of ASMs with novel or differentiated mechanisms of action, organized by molecular target, while placing recent regulatory successes and instructive failures within the broader transition toward mechanism-based, precision, and potentially disease-modifying therapies. Findings: A 2024 pipeline analysis identified more than 200 epilepsy therapies in preclinical or clinical development; at the cutoff of the present literature search (30 June 2026), over 40 compounds had reached phase II or III, with the majority directed at DEEs. Functional-state-selective sodium channel modulation has emerged as a leading conceptual advance supported by converging mechanistic and early clinical evidence, exemplified by relutrigine (PRAX-562), a preferential persistent-current inhibitor for which a regulatory decision is pending in SCN2A/SCN8A-DEEs, and vormatrigine (PRAX-628), whose large open-label effect was not reproduced in a controlled (blinded) trial. Several of the efficacy figures summarized here derive from congress presentations, interim analyses, or open-label extensions and await full peer-reviewed publication. Parallel advances include the selective Kv7 opener azetukalner; the dual-mechanism benchmark cenobamate; cholesterol-24-hydroxylase inhibition (soticlestat); selective serotonergic agonism (bexicaserin); glutamatergic precision agents (radiprodil); subtype-selective GABA_A modulators (darigabat, ganaxolone); and gene-directed therapies (zorevunersen, elsunersen). Pre-symptomatic intervention in tuberous sclerosis complex provides an early, single-trial clinical proof of principle for delaying and reducing the incidence of epilepsy in a genetically defined population; this should not yet be equated with established disease prevention. Conclusions: The pipeline reflects an ongoing shift from broad symptomatic agents toward mechanism-led, genotype-matched, and potentially disease-modifying treatments. This shift is tempered by a persistent translational gap between early signals and randomized-trial confirmation, and by the preliminary status of much of the supporting evidence. Full article

The Internet of Medical Things (IoMT) is a promising technology for enabling smart and efficient healthcare systems through continuous physiological monitoring, early anomaly detection, and proactive patient management. However, IoMT sensors are vulnerable to False Data Injection Attacks (FDIAs), in which adversaries can manipulate sensor measurements to compromise diagnostic accuracy, mislead clinical decision-making, and threaten patient safety. Existing detection approaches often rely on population-level statistical models that may not fully capture individual physiological variations or residual-based thresholds designed for relatively simple attack scenarios, limiting their ability to exploit the spatio-temporal dependencies of multi-sensor physiological streams and detect stealthy or adversarial FDIAs. This paper proposes a patient-specific FDIA detection framework based on a Graph Convolutional Network–Gated Recurrent Unit (GCN–GRU) digital twin that learns an individual patient’s normal physiological behaviour from clean baseline telemetry. The trained digital twin is integrated into a Kalman filter as the state prediction model, and the resulting standardised innovation residuals are used as detection features. To characterise stealthy attack behaviours, four complementary window-based feature groups are extracted from the innovation sequence: innovation statistics, sensor correlation drift, temporal smoothness, and uncertainty mismatch. A CNN-1D classifier is then trained to learn discriminative temporal attack patterns from these features for accurate detection. A structured attack taxonomy comprising five stealthy and adversarial FDIA scenarios is developed, where attacks are injected as smooth gradual or abrupt coordinated modifications to sensor measurements while remaining within plausible physiological ranges. Experiments conducted on the WUSTL-EHMS-2020 benchmark dataset demonstrate that the proposed framework achieves an F1-score of 94.3%, outperforming Isolation Forest and PCA Reconstruction by 34 percentage points. Furthermore, the proposed framework reduces the false alarm rate to 3.6%, compared with 35.1% and 9.2% achieved by Isolation Forest and PCA Reconstruction, respectively. These results demonstrate the effectiveness of the proposed framework for reliable detection of stealthy FDIAs in IoMT-based healthcare systems. Full article

Background: Cholangiocarcinoma (CCA) is a highly heterogeneous malignancy with limited therapeutic options and poor prognosis. The increasing complexity of molecular stratification and treatment selection has stimulated interest in computational and biological modeling approaches for precision oncology. Objective. This narrative review aims to provide a comprehensive overview of digital twins (DTs), DT-enabling computational models, and biological twins (BTs) in CCA, discussing their applications, limitations, and potential integration within hybrid precision medicine frameworks. Methods: A narrative literature review was conducted. To inform the twin-focused sections, a structured PubMed search was performed using predefined keywords related to CCA and twin-related technologies, including organoids, xenografts, organ-on-chip systems. Particular attention was devoted to recent studies addressing computational modeling, patient-derived experimental systems, and translational applications. Results: DT development in CCA is supported by an ecosystem of DT-enabling technologies, including radiomics, artificial intelligence, multi-omics integration, and simulation-based models. However, fully realized medical DTs remain unavailable. BTs, including patient-derived organoids, xenografts, and microfluidic platforms, enable functional validation of therapeutic hypotheses but face challenges related to scalability, standardization, and clinical feasibility. Emerging hybrid DT-BT frameworks seek to combine computational prediction with biological validation through iterative feedback loops, potentially improving patient stratification and treatment personalization. Conclusions: DTs and BTs represent complementary components of an evolving precision oncology ecosystem in CCA. Although technical, biological, regulatory, and implementation challenges remain, the convergence of computational models, longitudinal molecular monitoring, and patient-derived systems may facilitate clinically actionable hybrid twin frameworks. Successful translation will require both technological innovation and healthcare-system improvements to precision medicine access. Full article

This study presents a bibliometric analysis of AI healthcare research related to implementation science and governance frameworks from 2016 to 2026. A systematic search of the Scopus database identified 3780 peer-reviewed articles published across 1500 sources. The dataset was analyzed using Biblioshiny. The findings show an annual growth rate of 47.65% in governance-focused publications, exceeding the growth rate of technical AI research. Four main research themes were identified: regulatory compliance, ethical frameworks with limited operational measures, organizational readiness, and clinical workflow integration. The United States, China, and the United Kingdom are the leading contributors, while the Journal of Medical Internet Research and BMJ Open are among the main publication outlets. International collaboration (34.66%) remains concentrated among high-income countries. Thematic development has progressed from general ethical discussions to pandemic-related applications and more specific regulatory frameworks. Three research gaps contribute to the algorithm-to-policy translation deficit: the principles–practice gap, the regulatory–evidence gap, and the innovation–implementation gap. This study proposes an integrated governance framework based on five evidence-based principles. The framework is a conceptual model derived from bibliometric findings and requires further empirical validation in clinical settings before practical adoption. The study contributes by providing a bibliometric analysis of AI governance research and introducing the concept of the “algorithm-to-policy translation deficit” as an analytical framework. It also offers a structure to support future research and practice toward safe, effective, and equitable clinical implementation of AI. These findings guidance for regulators, healthcare organizations, AI developers, and researchers. Full article

Motor imagery (MI) classification serves as the backbone to brain–computer interfaces (BCIs) by strengthening the communication bridge between the human brain and external peripheral devices. The past two decades have witnessed unprecedented success in MI-BCIs, with applications not only in medical fields but also in several other domains, such as gaming and robotic control. Initially, MI classification primarily relied on classical signal processing techniques that were heavily impacted by signal variations; however, recent trends in deep learning (DL), specifically in sequence-oriented, attention-based, hybrid, and generative architectures such as recurrent neural networks (RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), and transformers have significantly improved the efficiency and robustness of MI classification. This study presents a comprehensive review of these sequence-oriented, attention-based, hybrid, and generative architectures including RNNs, VAEs, GANs, and transformers, comparing their robustness across various public MI datasets, highlighting their challenges, such as inter-subject variation, low signal-to-noise ratio (SNR), and the obstacles in real-time signal classification. We perform an in-depth analysis on the strengths and limitations of traditional models such as RNNs and LSTMs as well as emergent models such as VAEs and transformers, which have demonstrated superior performance in extracting the intricate patterns of the EEG data with low latency. Moreover, we critically examine the future potential of such models in overcoming current bottlenecks, such as weak generalization on unseen data and high computational load. This study aims to assist researchers in attaining significant insights into the state-of-the-art sequence, attention-based, hybrid, and generative models used in MI classification, thus offering a direction for future innovation. Full article

Computer-aided diagnostic technologies have demonstrated substantial advantages in 3D medical image segmentation, particularly in multimodal 3D medical image segmentation tasks, where they play a pivotal role in driving continuous innovation in related architectures. As an integration of U-Net and Transformer, the UNETR architecture has demonstrated remarkable efficacy in 3D medical image segmentation. Nevertheless, despite its successes, UNETR remains challenged by clinical complexities such as intricate tumor localization and anatomical structural diversity in complex clinical settings. To address these issues, we propose an enhanced 3D segmentation framework, UAtten-Unetr, designed to improve segmentation accuracy and robustness in complex medical scenarios. The framework captures global contextual information via hierarchical Transformer layers and incorporates a spatial–channel attention module to enable adaptive fusion of multimodal features, thereby effectively enhancing cross-modal feature alignment capabilities. Concurrently, we innovatively developed a unified loss function based on bimodal modality-specific Dice constraints and uncertainty regularization, optimized for synchronous learning across the ACDC (cardiac MRI) and AMOS22 (abdominal CT/MRI) datasets. Experimental results showed that UAtten-Unetr achieved an average Dice score of 92.20% on the ACDC dataset, exceeding the reported nnU-Net result of 91.61% by 0.59 percentage points. On the AMOS22 dataset, the proposed method achieved an average Dice score of 84.51%, exceeding the reported UNETR result of 78.33% by 6.18 percentage points. However, its myocardium Dice score (84.11%) was lower than those of nnU-Net (89.24%) and MT-UNet (89.04%), indicating a remaining limitation in myocardium boundary segmentation. These results indicate competitive segmentation performance under the reported experimental settings. This method delivers dual improvements in accuracy and generalization across complex anatomical scenarios, providing an effective solution for precise diagnosis in intricate clinical environments. Full article

Background: Cultivating medical students’ innovative behavior is crucial for talent development in medical education. However, the factors associated with medical students’ innovative behavior are still unclear. The aim was to examine factors associated with self-reported online innovative behavior of medical students. Methods: A cross-sectional electronic survey was conducted at a public medical university in China. A total of 371 medical undergraduates participated in the study. Data were collected with questionnaires on students’ abilities scales and analyzed with SPSS 26 and AMOS 26. The associations among constructs were analyzed through structural equation modeling. Results: Results showed that both digital resilience and technological self-efficacy were positively associated with medical students’ self-reported online innovative behavior. Statistically significant indirect associations were found between digital resilience and self-reported online innovative behavior through online learning engagement and self-directed learning behaviors. Statistically significant indirect associations were also found between technological self-efficacy and self-reported online innovative behavior through online learning engagement and self-directed learning behaviors. Conclusions: Digital resilience and technological self-efficacy were directly and indirectly associated with medical students’ self-reported online innovative behavior in online learning environments. The results provide useful empirical insights for understanding students’ self-reported online innovative behavior in online learning environments. Full article

Background: Ocular drug delivery is hindered by the eye’s complex anatomy and multiple physiological barriers. Because of their biocompatibility, biodegradability, and capacity to prolong drug retention, hydrogels have emerged as promising platforms for ocular drug delivery. This study provides a bibliometric and visual analysis of research on hydrogel-based ocular drug delivery from 1991 to 25 August 2025. Methods: Publications were retrieved from the Web of Science Core Collection and screened for relevance. CiteSpace, VOSviewer, and the Bibliometrix R package were used to analyze annual publication trends, contributions and collaborations among countries, institutions, and authors, core journals, and evolving research themes. Results: A total of 1354 publications (991 articles and 363 reviews) were included. Annual output increased markedly after 2007–2008, reflecting rapid growth in this field. China and the USA were the leading contributors, and the University of Florida was the most productive institution. Keyword co-occurrence, clustering, and burst analyses identified in situ hydrogels, tissue engineering applications, and anti-VEGF delivery as major research hotspots. Conclusions: This study maps the growth and diversification of hydrogel-based ocular drug delivery research, revealing a shift toward disease- and segment-specific applications, with controlled-release, stimuli-responsive systems, injectable hydrogels, and anti-VEGF as emerging research priorities. Full article

Introduction: Crohn’s disease (CD) remains associated with long-term morbidity despite biologic therapies. Surgery, historically considered a last-resort option, is increasingly integrated into disease management earlier. This review examines the evolving role of surgery in the biologic era. Methods: A focused narrative review of randomized controlled trials, meta-analyses, observational studies, and international guidelines addressing contemporary surgical strategies in CD was performed. Results: The LIR!C trial demonstrated that early ileocecal resection provides durable remission and improves long-term outcomes compared with biologic therapy in selected patients. The PISA II trial supported an early combined surgical and medical approach for perianal fistulizing disease. Preoperative optimization, including nutritional support and adjustment of immunosuppressive therapy, has become a cornerstone of perioperative management. Laparoscopic surgery remains the preferred approach whenever feasible, while robotic surgery is emerging as a promising platform that is expected to play an increasingly important role in the surgical management of CD. Postoperative recurrence remains a major challenge, prompting the development of innovative surgical strategies. Although the Kono-S anastomosis initially showed promising reductions in recurrence, recent prospective studies failed to confirm superiority over conventional techniques. Similarly, extended mesenteric excision did not demonstrate improved outcomes despite increasing evidence implicating the mesentery in CD pathogenesis. Strictureplasty remains an effective option for selected fibrotic small-bowel strictures. Conclusions: Surgery remains central to multidisciplinary CD management and offers the potential to modify disease when performed early in selected patients. The impact of innovative surgical strategies on postoperative recurrence remains uncertain, and ongoing trials are expected to further help with surgical decision-making. Full article

Background/Objectives: Benchmark performance does not establish that a wrist-wearable stress measure is valid in healthcare workflows. We tested whether binary stress-versus-non-stress performance reflects stress-specific physiology rather than heart-rate (HR) arousal, as well as whether models generalize from wellness-like corpora to a clinical workforce context. Methods: We analyzed three public wrist Empatica E4 corpora: Wearable Stress and Affect Detection (WESAD), Stress-Predict, and a real-world Nurse dataset. An iso-heart-rate (iso-HR) evaluation balanced the HR within subjects before measuring non-cardiac discriminability. We compared a continuous skin conductance response (SCR) count with logistic regression, gradient boosting, four unsupervised domain-adaptation methods, and an accelerometer context-aware variant, under leave-one-subject-out and leave-one-dataset-out evaluation with subject-cluster bootstrap intervals. Results: The single SCR score was statistically indistinguishable from logistic regression in paired cross-corpus tests, and no adaptation method moved a weak corpus into a usable range: across all evaluated models, the transfer area under the receiver operating characteristic curve (AUROC) was approximately 0.90 with WESAD as the target but 0.52–0.57 with Stress-Predict or Nurse. Adding activity context raised the within-corpus AUROC yet lowered cross-corpus transfer, because the movement–stress association reverses across protocols. On Nurse, source-trained models reached AUROC 0.541–0.559 against prevalence 0.796 with poor calibration, and conditioning on movement or on distance to the source distribution removed the residual discrimination. Iso-HR evidence was diagnostic mainly in WESAD. Conclusions: Neither shallow unsupervised adaptation nor wrist activity context established reliable transfer to Nurse, and sparse retrospective labels prevent attributing this solely to physiological non-transfer. Cross-corpus and iso-HR controls, not model complexity, determine what can be concluded for healthcare workflows. Full article

This paper assesses the impact of uncertainty for investors on the economic valuation of medical innovation projects for orphan drugs or rare diseases. Conventionally, investor evaluation uses the deterministic discounted cash flow (DCF) method with an appropriate sensitivity analysis that captures some level of uncertainty. In healthcare, and particularly for rare diseases, the levels of uncertainty in financial outcomes (return on investment and net present value (NPV)) are broader than the ones normally captured by the DCF formula. Uncertainties include R&D costs, the approval process (level and timing) for obtaining reimbursement, sales, the production cost, and the failure probabilities of the clinical trial phases, to name a few. Additionally, there is not only one type of investor to consider, but different investors exposed to different levels of risk management of their investment. Our analysis tried to capture those two dilemmas (higher levels of uncertainty and different investor types) in two ways. One way was to identify a better method to enhance the different levels of uncertainty. The real option method of evaluation was proposed instead of DCF. For instance, the real option method better captures the uncertainty of the different phases of product development. The other way is to differentiate the investor types through their level of risk assessment perspectives. Small investors and start-up companies may see more benefit in applying the real option methodology to estimate their NPVs at different time points during product development. In summary, our evaluation identified various types of uncertainty when assessing an investment, along with methods to manage their effect on the economic/financial outcomes of medical innovations. Given the high uncertainty associated with early-stage drug development, such as orphan drugs for rare diseases, the real options approach is preferable to traditional DCF models. The analysis also showed that there is not just a single investor perspective to consider but specific perspectives that enhance the prime use of the real option methodology. Full article