Search Results (1,955)

Human Digital Twins (HDTs) extend traditional Digital Twin (DT) concepts by modeling both humans and hospital processes to support smarter and more human-centered healthcare. By integrating Industry 4.0 (I4.0) technologies with the human-centric principles of Industry 5.0 (I5.0), HDTs offer new opportunities to improve hospital operations. This study presents a PRISMA-based systematic literature review to examine the role of HDTs in hospital operations. A total of 329 papers were identified through the initial search, and after the screening process, 22 studies were included for in-depth analysis. The review combines bibliometric analysis to examine publication trends, leading authors, contributing countries, and keyword co-occurrence with a content analysis to identify the main research themes. Three major themes emerged: (1) HDT architectures and data integration, (2) human-centric and governance aspects, including explainable artificial intelligence and privacy, and (3) operational and clinical outcomes, including patient flow, resource utilization, and staff support. Based on these findings, the study proposes a four-layer HDT framework for practical implementation in hospital operations. Although the reviewed studies indicate that HDTs have considerable potential to improve operational efficiency and strengthen human involvement, most existing research remains conceptual or simulation-based. Future research should therefore prioritize real-world implementation and validation while incorporating ethical, explainable, and sustainable design principles. Full article

Recent industry reporting indicates that meantime to exploit has become negative in several observed datasets, implying that exploitation may occur before patch availability for some classes of vulnerabilities. Adversarial use of artificial intelligence (AI) is a documented accelerant of this trend. This paper addresses the operational problem that follows in healthcare cybersecurity: the volume and velocity of vulnerability disclosure exceed human analytic capacity, which leads practitioners to under-prioritize, or defer entirely, individual Common Vulnerabilities and Exposures (CVEs) at precisely the moment their risk is rising. The study develops and evaluates a purposeful information technology artifact intended to resolve this problem within a mid-sized United States healthcare system. The artifact is a three-application automated CVE intelligence, prioritization, and remediation-tracking pipeline implemented in Microsoft Azure Logic Apps, integrating the National Vulnerability Database (NVD), the CISA Known Exploited Vulnerabilities (KEV) catalog, the Microsoft Security Response Center (MSRC) CVRF API, Microsoft Defender, Claroty xDome, Microsoft Security Copilot, and ServiceNow, and operationalizing the four risk factors codified in CISA Binding Operational Directive (BOD) 26-04. In naturalistic operations across six CISA Weekly Vulnerability Summary bulletins, the artifact processed 12,855 unique CVE references and reduced them to 1640 environment-relevant findings, an 87.2 percent exposure-first reduction, before expensive per-CVE enrichment and ticketing. The findings indicate that governed automation demonstrably increases CVE coverage, reduces low-value enrichment volume, and produces a deterministic, BOD 26-04-conformant prioritization that is fully traceable in the SharePoint tracker, where every assigned tier is reconstructable from its KEV, ransomware, xDome-exploited, EPSS, CVSS, and exposure inputs. Because no controlled before-and-after time-and-motion study was conducted and no independent ground-truth exploitation labels were collected, three distinct outcomes remain future validation targets rather than demonstrated results: analyst productivity, comparative predictive prioritization accuracy against independent ground-truth exploitation outcomes, and remediation speed. The contribution reported here is therefore operational scale, coverage, and auditable prioritization traceability, not measured improvement in analyst decision-making or patient-safety outcomes. Full article

Wearable brain-imaging devices have been developed to meet the growing demand in the healthcare industry for long-term monitoring of brain signals in natural conditions, such as monitoring brain diseases and emotions. However, conventional EEG and fNIRS (functional near-infrared spectroscopy) devices are often expensive, bulky and difficult to operate, making it difficult to monitor patients for long periods in natural conditions. To address these issues, this article proposes a low-cost, portable and multimodal wearable brain signal acquisition scheme. It combines EEG (electroencephalography) and fNIRS to reflect brain activity from different perspectives. In order to make it more wearable, a conductive rubber material is used as the electrode for the EEG. In this study, the corresponding experiments were used to verify the performance of the device. The first is the measurement of internal system noise, which satisfies the data acquisition of EEG and fNIRS at different gain levels. The α-rhythm experiment and the SSVEP (steady-state visual evoked potentials) experiment were used to validate the performance of EEG data acquisition. The performance of the fNIRS was verified by measuring changes in cerebral blood oxygen during breath-hold and breathing. In addition, by decomposing the raw fNIRS data with the VMD (variational mode decomposition) algorithm and performing correlation analysis, heart rate information was separated from the data. The performance of the proposed device was validated in the above experiments, confirming the feasibility of the design for multimodal data acquisition and meeting the requirements for portability and wearability. Furthermore, the proposed device was tested with 31 subjects (15 depressive subjects) to detect depression. Experiments proved the effectiveness of the multimodal signals, which outperformed single modal and surpassed EEG by 8.4% and fNIRS by 23.5%. Full article

Background: Malaria is a significant public health challenge in the Democratic Republic of the Congo. In conflict-affected areas, access to quality-assured ACTs is restricted by chronic stockouts and circulation of substandard and falsified antimalarials. This study investigates perceptions and social acceptability of Artemisia annua herbal tea in the Kalima Health Zone. Methods: Distinct from the prior provider-arm cross-sectional survey of the same program in the Kalima Health Zone which sampled only biomedical healthcare workers (n = 337), this exploratory qualitative study adopted a socio-anthropological design across three stakeholder strata (community users, biomedical providers, opinion leaders). Maximum-variation purposive sampling recruited 30 informants across five health areas (11 end-users, 11 providers, 8 opinion leaders). Sample-size adequacy was assessed against Malterud’s information power framework; data collection continued until operational thematic saturation. Hybrid inductive–deductive thematic content analysis followed COREQ reporting. Results: End-users demonstrated high willingness, driven by accessibility, perceived three-day recovery, and avoidance of unreliable ACTs. Biomedical providers voiced intense posological anxieties over uncalibrated raw biomass and sub-therapeutic dosing. Behavioral patterns were shaped by socio-religious norms and digitally mediated religious discourse. A unanimous demand for industrial galenic transformation of raw biomass into calibrated tablets emerged across all groups. Conclusions: This study does not validate the clinical efficacy, safety, posological adequacy, or resistance-attenuation potential of Artemisia annua herbal tea. It documents a transversal household-level consensus, across the three strata and five health areas, that Artemisia annua herbal tea adoption responds to documented double ACT supply chain failure. A unanimous demand for industrial galenic transformation emerged across all stakeholder groups. Full article

Medical tourism is among the fastest-growing segments of the global travel industry, and Taiwan’s internationally recognised healthcare system positions it well to compete in this market. Yet the companies best placed to lead this development have traditionally been identified through online word-of-mouth or personal recommendation rather than objective evidence, and performance criteria are rarely weighted to reflect both subjective opinion and objective data. This study addresses both gaps with a hybrid framework that uses main path analysis to identify, objectively, the companies most active in medical aesthetic technology, and integrates the Best–Worst Method (BWM), Entropy, Game Theory, and Grey-based TOPSIS to weight a four-dimensional, seventeen-criterion framework and rank company performance in medical tourism. An empirical application to seven Taiwanese companies engaged in medical aesthetics alongside medical tourism, drawing on industry expert assessments, shows that Service and Healthcare Resources is the most influential dimension, and that cloud-based customisation, multilingual personnel, and professional in-trip medical care are the most important criteria. Sensitivity analysis across nine weighting scenarios and comparison against four alternative ranking methods confirm the robustness of the ranking. The framework offers a replicable, objective template for evaluating medical tourism performance and provides managerial guidance for companies and policymakers seeking to strengthen Taiwan’s position in this market. Full article

Cyber-Physical Systems (CPS) are using more AI for smart decisions and automation, but also faces new security issues. This study surveys existing CPS security assessment methodologies across healthcare, automotive, energy, and critical infrastructure, identifying their limitations in addressing emerging digital-physical threats. We find that traditional risk assessment and testing approaches, often network-centric and compliance-driven, are insufficient for AI-powered CPS. New vulnerabilities arise from the tight coupling of cyber and physical components, such as adversarial manipulation of sensors that can cause dangerous misbehavior, supply chain attacks on AI models, and the inability to patch critical devices on the fly. We use AI techniques and problem-solving methods to improve the security of CPS. The framework helps detect threats, monitor system activities, and reduce security risks in real time. It also follows important security and privacy standards such as NIST, IEC 62443, ISO 21434, and GDPR. The system continuously checks CPS operations, uses AI tools to find weaknesses, and supports security compliance. We also study real-world CPS attacks, including industrial malware, car hacking, and medical device attacks, to show the importance of the framework. In this research, we present prototype implementation and experimental evaluation along with a case study of protecting a smart manufacturing plant during a ransomware attack using the proposed approach. Full article

The design of healthcare facilities must comply with applicable regulatory requirements, which significantly influence functional, spatial, and performance aspects of healthcare buildings. However, compliance checking is still predominantly conducted through manual procedures, leading to time-consuming workflows and a high risk of errors. In this context, Computational Compliance Checking (CCC) offers the potential to enhance the efficiency and reliability of regulatory verification processes. The adoption of Building Information Modelling (BIM), supported by the open standard Industry Foundation Classes (IFC), enables the formalisation of Information Delivery Specifications (IDS) as machine-interpretable rules for model validation. Additionally, the buildingSMART Data Dictionary (bSDD) contributes to the semantic structuring and classification of information requirements. This study explores how the IDS standard can enhance CCC processes by supporting the automated verification of regulatory requirements. Complementary solutions based on Python are proposed to address cases where IDS alone proves insufficient. A methodology for the semi-automated compliance checking of healthcare regulatory requirements is developed and applied to a case study based on regional regulations in the Veneto Region (Italy). Tested on the inpatient ward of a hospital BIM model, the procedure processed all 35 accreditation requirements, 91.4% through IDS and 8.6% through complementary Python routines, revealing several design non-conformities. Full article

Blockchain is one important building blocks of the Internet of the future, called Web3. The Blockchain technology supports a wide range of applications, spanning from Smart Cities and automotive industries, from agriculture to energy. The healthcare sector, in particular, has experienced a profound impact from blockchain-based technologies, paving the way for the development of true digital healthcare systems. By enabling secure and immutable data storage, and facilitating the sharing of this information among all nodes possessing a local copy of the distributed ledger, blockchain plays a vital role in the analysis of healthcare data. This paper provides a comprehensive survey of the main blockchain platforms utilized in the digital healthcare, integrated with a comparative analysis. In addition, the implementation of Innovative permissioned Blockchain for eHealth (IBEH) is presented and discussed in detail. IBEH addresses key challenges in digital health data management, including secure and controlled access to sensitive health information, ensuring data integrity and traceability, and secure sharing between different healthcare institutions and organizations. This is made possible by decoupling the application and blockchain layers and by a flexible, customizable, and easily deployable infrastructure. IBEH integrates the application-oriented and embedded layer with that of a blockchain network built with the MultiChain platform, which uses smart contracts with permissions, REST APIs, and RPC calls. The main features and its associated smart contracts within the healthcare domain are discussed. Finally, the analysis of performance is provided. Full article

Industrial hemp is a chemically rich plant increasingly explored within sustainable production systems aimed at the full utilization of all plant fractions. While hemp inflorescences have been extensively investigated, the biological potential of seed-depleted infructescences remains largely underexplored. This study compared essential oils (EOs) distilled from inflorescences and seed-depleted infructescences of hemp (Cannabis sativa L. cv. Futura 75). Their chemical composition was determined by gas chromatography–mass spectrometry (GC–MS), and their cytotoxicity, hemocompatibility, effects on blood coagulation, and antibacterial activity against a panel of 11 Gram-positive, Gram-negative, and microaerophilic bacterial strains were evaluated. The major EO constituents included α-Pinene, (E)-Caryophyllene, Myrcene, α-Humulene, Caryophyllene oxide, and Cannabidiol. EOs obtained from seed-depleted infructescences exhibited distinct chemical profiles, low cytotoxicity toward BJ human skin fibroblasts, minimal haemolytic activity, no significant effects on blood coagulation, and, in most cases, stronger antibacterial activity than inflorescence-derived EOs. Particularly high activity was observed against skin- and oral-associated bacteria, including Cutibacterium acnes and Streptococcus species. These findings demonstrate that seed-depleted infructescences represent underutilized post-harvest biomass and a valuable and sustainable source of biologically active EOs, supporting their further investigation for potential pharmaceutical, cosmetic, and oral healthcare applications. Full article

The progressive integration of Whole Slide Imaging (WSI) technology and Artificial Intelligence (AI) architectures is driving a structural transformation in pathology and precision oncology. This structured critical review analyzes and systematizes the impact of this technological transition along two fundamental operational dimensions of the modern biopharmaceutical industry: pre-registration Clinical Research and post-launch strategies governed by Medical Affairs. The first section explores how computational pathology is improving efficiency and reducing risk in drug development. Replacing analog visual assessment—intrinsically subject to inter-observer and intra-observer variability—with quantitative algorithms for cellular classification and segmentation enables optimization of patient recruitment in clinical trials, reducing screening failure rates. This review also examines the emerging role of Spatial Biology in extracting complex topological metrics from the Tumor Microenvironment (TME) and the use of AI for the objective and auditable quantification of critical surrogate endpoints, such as Pathological Complete Response (pCR), while acknowledging that algorithmic precision remains sensitive to pre-analytical variables and dataset biases. In the second section, the study investigates the strategic evolution of Medical Affairs, acting as a vital scientific communication and translational bridge between the complexity of Data Science and clinical hospital practice. Challenges related to AI adoption by clinicians are examined, emphasizing the importance of educational programs based on Explainable AI (XAI) to overcome the cognitive limitations of the black-box paradigm and the complex regulatory validation pathway for Software as a Medical Device (SaMD) under the stringent European IVDR framework—supported by an analysis of historical regulatory benchmarks such as the Paige Prostate case. The paper also explores the potential of AI in the large-scale generation of Real-World Evidence (RWE), applied to the creation of synthetic control arms in pharmacoeconomic settings. In conclusion, the study highlights that the diagnostic algorithm has ceased to be merely a laboratory support tool and has become a strategic asset and an integral adjunct to therapeutic decision-making. Overcoming current challenges related to data privacy through Federated Learning architectures, together with the imminent transition toward Foundation Models, foreshadows a fully data-driven healthcare ecosystem, making continuous skills development (digital upskilling) an essential requirement for professionals in the biopharmaceutical sector. Full article

Background/Objectives: The buccal mucosa offers a promising non-invasive route for systemic drug delivery, particularly for hydrophilic compounds like captopril (CAP), which exhibit low permeability and are subject to gastrointestinal instability and first-pass metabolism. This study aimed to develop and characterize captopril-loaded, chitosan-based mucoadhesive buccal films with different permeation enhancers and to evaluate their physicochemical properties, drug release, cytocompatibility, and in vitro transport across a TR146 buccal epithelial cell model. Methods: Films were prepared by the solvent-casting method using chitosan as the film-forming polymer. Different enhancers were investigated, including organic acid salts of chitosan (ascorbate, citrate, and lactate) and chemical permeation enhancers (sodium lauryl sulfate, polyethylene glycol 400, Span 20, and EDTA). Results: The resulting films exhibited acceptable thickness, moisture content, appropriate mechanical properties, and good mucoadhesive strength. In vitro dissolution studies demonstrated rapid CAP release, with >50% released within 15 min and near-complete release by 180 min across all formulations. Cytotoxicity assessment via a Neutral Red uptake assay in TR146 cells confirmed high cell viability (>81%) after 4 h of exposure, indicating good biocompatibility. In vitro permeation experiments revealed that films prepared with chitosan ascorbate and chitosan lactate enhanced CAP transport compared to other formulations, achieving the highest flux and apparent permeability coefficients. Conclusions: These findings demonstrate that chitosan ascorbate and lactate salts effectively improve the buccal permeability of captopril while maintaining good film properties and biocompatibility. This work highlights the potential of chitosan ascorbate- and lactate-based mucoadhesive films as an efficient platform for the buccal delivery of CAP. Full article

Lycium barbarum polysaccharides (LBPs), the primary bioactive components extracted from the traditional medicinal and edible plant Lycium barbarum L., have attracted extensive attention in biomedical research due to their superior anti-inflammatory, immunomodulatory and biological safety properties. Inflammation is a key pathological basis of various chronic metabolic and immune diseases, and LBPs can exert targeted regulatory effects on inflammatory responses through multiple molecular pathways. This article systematically reviews the latest advances in extraction technologies and structural characterization of LBPs, and illustrates their regulatory mechanisms against inflammation in different organs. Relevant toxicological studies are also summarized to confirm their low toxicity and safety. Additionally, it comprehensively concludes the current progress of LBPs in healthcare products, pharmaceutical development, and related patent innovations. Future perspectives highlight green efficient extraction, structural modification, targeted delivery, and in-depth mechanism exploration, aiming to provide a comprehensive theoretical basis for the further development and industrial application of LBPs. Full article

The rapid deployment of Internet of Things (IoT) devices across smart cities, healthcare systems, industrial automation, transportation networks, smart grids, and cyber-physical infrastructures has expanded the modern cyberattack surface. IoT devices are often constrained by limited processing capacity, memory, battery power, and communication bandwidth, making conventional security mechanisms difficult to deploy consistently at scale. Intrusion detection systems (IDSs) provide an important defensive layer; however, many machine-learning-based IDSs are developed under static assumptions and may experience performance degradation as traffic distributions evolve due to firmware changes, device onboarding, protocol updates, user behavior variation, or adaptive attacks. This paper presents a hybrid IDS framework that integrates supervised Random Forest classification, unsupervised Isolation Forest anomaly monitoring, and Kolmogorov–Smirnov (KS)-based concept drift monitoring. In the experimental pipeline, Isolation Forest is trained exclusively on benign traffic to ensure that the anomaly detector models normal behavior rather than an attack-dominated training distribution. The evaluation uses a large-scale chronologically sampled subset of the CICIoT2023 dataset containing 3,890,621 records while preserving the natural class distribution of 2.35% benign traffic and 97.65% attack traffic. The chronological 80/20 train/test split is established first at the file level, followed by systematic sampling within each split to reduce the risk of leakage across the evaluation boundary. On the 746,094-record test set, the proposed hybrid IDS achieved 99.73% accuracy, 99.89% precision, 99.83% recall, 99.86% F1-score, and a false positive rate of 4.77%. The corresponding confusion matrix contains TN = 16,683, FP = 836, FN = 1205, and TP = 727,370, yielding 95.23% specificity and 97.53% balanced accuracy. Standalone Random Forest marginally outperformed the hybrid model in raw accuracy and false positive rate; therefore, the contribution of the proposed framework is centered on deployment-oriented anomaly monitoring, drift awareness, and generalization rather than absolute superiority in static classification metrics. A leave-one-attack-family-out experiment withholding MITM-ArpSpoofing from training showed that the hybrid model detected 85.26% of the unseen attack-family samples, compared with 85.18% for Random Forest alone and 7.05% for Isolation Forest alone. These findings provide initial evidence of generalization to one held-out attack family but should not be interpreted as proof of broad zero-day detection capability. The framework is therefore positioned as a competitive IDS that combines supervised detection with anomaly monitoring and concept drift awareness for deployment-oriented IoT security. Full article

The rapid adoption of Internet of Things (IoT)-enabled applications has significantly expanded the cyberattack surface across a wide range of critical systems such as industrial IoT (IIoT), smart grids, transportation, healthcare, industrial control systems, and smart cities. False data injection attack (FDIA) has emerged as a serious security threat to these applications due to its stealthiness and adversarial nature, silently corrupting the data integrity of critical operational processes without triggering conventional detection mechanisms. Existing FDIA solutions rely on single-model architectures that are built based on classical or limited predefined attack scenarios. Such solutions often fail to achieve robust detection under adversarial and evolving attack conditions; accordingly, they lack generalisability and are insufficient to capture the broader scope of FDIAs. In this study, a hybrid detection framework is proposed that integrates a Random Forest classifier with an unsupervised anomaly detection model based on a deep autoencoder combined through a Logistic Regression metaclassifier. The proposed framework addresses the gap in single-model detectors that either rely on fixed decision boundaries that struggle with gradually evolving stealthy FDIA patterns or on anomaly detection that lacks strong discriminative power in separating subtle adversarial deviations from normal operational variability. Different types of stealthy and adversarial FDIA have been modelled and injected into the dataset samples for use in training the proposed model. The results show that the overall detection performance of the proposed architecture improved by 2.39 percentage points in terms of F1-score while maintaining a low false-positive rate of 0.49%. These findings reflect the effectiveness of feature representation learning via autoencoders and hybrid classification strategies against stealthy and adversarial FDIA patterns. Future work should include temporal modelling for further advancing robust detection against evolving adversarial threats. Full article

Artificial intelligence (AI) is transforming healthcare and the built environment, yet its application to healthcare real estate (HRE) remains fragmented and poorly understood. This study systematically reviews AI applications across the HRE asset lifecycle to identify evidence gaps and evaluate their potential to improve decision-making, operational performance, and sustainable healthcare infrastructure. Following the Joanna Briggs Institute methodology and PRISMA-ScR guidelines, the search identified 2881 records, of which 87 studies met the inclusion criteria. Building on the evidence gaps identified through this mapping, this study develops conceptual contributions, including a lifecycle maturity index, the Algorithm-to-Asset-Value Translation Chain, and the AI-HREDF, that serve as theoretically grounded, testable proposals for future empirical investigation. Each study was classified by lifecycle stage, evidence directness, and evidence strength. Only 14 studies (16%) provided direct evidence linking AI to HRE decisions, while most focused on operations and facility management, leaving major gaps in site selection, planning, construction, and investment. This review identifies three evidence translation gaps that prevent AI advances from becoming measurable improvements in asset performance and financial value. To address these challenges, we propose the AI-Integrated Healthcare Real Estate Decision Framework (AI-HREDF), the Algorithm-to-Asset-Value Translation Chain, and a research agenda for future work. The findings provide a foundation for integrating AI into healthcare infrastructure planning, management, and investment while supporting more resilient, resource-efficient, and sustainable healthcare facilities. Full article