Search Results (890)

The rapid digitalisation of healthcare has accelerated the adoption of telemedicine, Electronic Health Records (EHRs), and the Internet of Medical Things (IoMT), transforming healthcare delivery into a highly interconnected and patient-centric ecosystem. In response to growing concerns about data security, privacy, and interoperability, blockchain technology has emerged as a promising solution for its decentralization, immutability, auditability, and secure access control. However, many existing blockchain infrastructures rely on classical cryptographic primitives, including RSA- or elliptic-curve-based public-key mechanisms and cryptographic hash functions such as SHA-256, whose relevant security properties may be affected by sufficiently powerful quantum attacks. This review investigates the convergence of blockchain and quantum technologies to address emerging security threats in e-health systems. A structured literature review was conducted in accordance with the PRISMA 2020 guidelines using the IEEE Xplore, PubMed, ACM Digital Library, Google Scholar, and Crossref databases, covering studies published between January 2018 and June 2025. Following a systematic screening and eligibility-verification process, 57 relevant studies were selected and analyzed. The review evaluates quantum-resilient security mechanisms, including Quantum Key Distribution (QKD), Quantum Random Number Generation (QRNG), and NIST-standardized Post-Quantum Cryptography (PQC) algorithms specified in FIPS 203, FIPS 204, and FIPS 205. Based on the identified research gaps in the state of the art, this study also proposes a novel four-layer Quantum-Blockchain Security Architecture (QBSA) designed for secure healthcare environments. The analysis further reveals significant challenges associated with lightweight PQC deployment for IoMT devices, interoperability standardization, quantum hardware limitations, and regulatory compliance in cross-institutional healthcare systems. The findings highlight the necessity of integrating quantum-resilient cryptographic frameworks with blockchain infrastructures to support the development of secure, scalable, and patient-centric next-generation e-health ecosystems. Full article

Food–medication interactions can occur when medicines are taken with foods, drinks, herbs, or supplements that influence drug absorption, exposure, or activity. Screening these combinations is challenging because the available evidence is imbalanced, prescription text may be recognized incorrectly, unsupported inputs may produce unreliable predictions, and altered software artifacts may change the recommendation presented to the user. iMediFood-Shield addresses these concerns through an evidence-first edge-AI framework that combines structured diet–drug interaction evidence, prescription-assisted medication confirmation, coverage-aware rejection, calibrated five-class prediction, false-safe-aware confidence gating, and software-based tamper-evident verification. The DDID preparation process began with 23,950 evidence records and produced 16,644 canonical medication–food/herb pairs, including 16,165 single-effect model-eligible pairs and 479 multi-effect conflict pairs. A leakage-free 70%–15%–15% split was applied after canonicalization, and the deployed lookup was restricted to training-supported and conflict records. On the operational locked-test AI branch of 2259 supported unseen pairs, the final calibrated LinearSVC with the validation-selected MedSafe-GATE threshold of 0.65 achieved 91.72% accuracy, 80.57% balanced accuracy, and a macro F1-score of 0.8359. The gate reduced calibrated false-safe predictions from 55 to 28, corresponding to a 49.09% reduction and a final false-safe rate of 1.35% among interaction-bearing AI-branch pairs. RxOCR-Guard achieved 94.67% candidate recall and 100.00% candidate precision on a controlled synthetic prescription benchmark, while mandatory user confirmation was retained because top-1 candidate accuracy was 51.33%. The unchanged baseline and all ten adverse software-bundle conditions produced the expected verification outcomes for artifact-modification, missing-file, key-mismatch, manifest-alteration, and rollback cases. Raspberry Pi deployment reproduced all 2259 reference predictions without mismatch, completed covered AI inference in 1.737 ms on average, and verified the protected software bundle in 80.249 ms on average. These results show that iMediFood-Shield can combine evidence-grounded screening, conservative AI decision control, prescription confirmation, and software-integrity verification within a resource-constrained edge research prototype. Full article

Artificial intelligence (AI)-based digital twins (DTs) are emerging as a groundbreaking paradigm in rehabilitation and physiotherapy, enabling the creation of dynamic virtual representations of patients for continuous monitoring, prognostic assessment and personalised therapeutic interventions. This article presents a global, interdisciplinary review of AI-based DT technologies in rehabilitation settings utilising the Internet of Medical Things (IoMT), with particular emphasis on the integration of wearable and implantable sensor systems in next-generation wireless healthcare applications. The article analyses how multimodal wearable sensors, implantable devices and smart wireless communication networks can support the acquisition of real-time biomechanical and physiological data for adaptive rehabilitation. By combining perspectives from biomedical engineering, physiotherapy, computational intelligence and wireless healthcare systems, this article highlights the emerging opportunities and challenges associated with the creation of scalable digital twin ecosystems for precision rehabilitation. The proposed vision contributes to the development of smart, connected and personalized rehabilitation infrastructures, in line with future paradigms of healthcare and wireless communication. Full article

Background: Wearable sensing and local wireless communication architectures offer significant potential for accessible rehabilitation technologies. The objective of this exploratory study was to design, implement, and describe an interdisciplinary, wireless inertial sensing platform—the Virtual Therapist Platform (VTP)—for real-time postural monitoring and exergame-based rehabilitation in a controlled laboratory setting. Methods: A laboratory proof-of-concept evaluation was conducted across three integrated domains: (1) technical characterisation of the dual-node ESP-NOW architecture (30-min test at 50 packets/s per node via an ESP32-C6 gateway) for real-time trunk tracking; (2) formative usability and acceptability assessment of the VTP interface across two independent cohorts of fourth-year Physiotherapy students ($n=25$ per cohort; 2024 and 2025) using a standardized protocol; and (3) a descriptive mapping of the four-phase interdisciplinary development process integrating Physiotherapy and Mechatronics Engineering inputs. Results: The two-node ESP-NOW test demonstrated high communication performance (99.68% packet delivery ratio (PDR), 8.03 ms mean latency, 1.98 ms jitter, and 99.68 ± 0.52 packets/s throughput). Subjective usability increased from a median score of 85.0 (IQR: 80.0–92.5) in 2024 to 100.0 (IQR: 100.0–100.0) in 2025. Conclusions: The dual-node architecture provided reliable local ESP-NOW packet transmission, while subjective evaluations demonstrated high system usability and acceptability. This interdisciplinary baseline lays the groundwork for future evaluations in clinical environments. Full article

IoT-connected healthcare sensor networks require authenticated and privacy-preserving data exchange among wearable sensors, mobile medical terminals, edge gateways, cloud servers, and medical institutions. Existing authentication schemes for healthcare IoT often bind signatures directly to user identities, exposing sensitive personal or institutional information and imposing heavy verification costs on resource-constrained sensing devices. To address this problem, we propose a fine-grained and flexible dual authentication scheme for healthcare sensor networks. In the proposed scheme, health data and diagnoses are signed with a fresh signing key and a fine-grained access control policy each time, so that the signer identity remains hidden while authorized entities can still modify permitted parts of signed data. No entity other than an authorized entity can trace a malicious signer or modify signed data without changing the data source. To support lightweight verification in sensor-edge-cloud deployments, we further present a verifiable outsourced authentication scheme that outsources time-consuming pairing operations to cloud servers; the online verification process then requires only six multiplication operations. As a fundamental technical component, we present a practical attribute-based sanitizable signature with shorter signature and key lengths and more efficient signing and signature-changing operations than the state-of-the-art policy-based sanitizable signature (P3S). Formal security analysis and experiments demonstrate the security and practicality of the proposed scheme for privacy-preserving healthcare sensing and medical data exchange. Full article

This paper presents the hardware aspects of a Hjorth-parameter deep neural network (DNN)-based cardiac fibrillation diagnosis pipeline targeting low-power Internet of Medical Things (IoMT) devices and ASIC implementations. Building on earlier edge-to-cloud studies where Hjorth activity, mobility, and complexity were computed in software on microcontrollers and classified by a floating-point DNN, we introduce a fully synthesizable fixed-point hardware module that computes these parameters in real time, together with a quantized neural network (QNN) operating directly on the resulting fixed-point features. The Hjorth block includes derivative generation, accumulator banks, variance computation, and hardware divider and square-root units. Using the Shandong Provincial Hospital Database (SPHD) as in our previous work, we evaluate the impact of end-to-end fixed-point quantization on the AF-related arrhythmia detection performance for bit widths between 6 and 16 bits. For 10–12-bit configurations, the quantized pipeline achieves accuracy of approximately 93.7% and an area under the ROC curve (AUC) above 0.97, closely matching the floating-point baseline while significantly reducing the arithmetic complexity and memory footprint. ASIC synthesis in a 28 nm CMOS standard-cell library shows that the complete atrial detector core, integrating the Hjorth extractor and the hardware fully connected QNN, occupies on the order of $2\times {10}^4\mathsf{\mu }{\mathrm{m}}^2$ and dissipates about 3 mW, with a critical-path delay of 7.08 ns. For the optimal 10–12-bit operating points, the synthesized core achieves per-inference energy in the range of 18.6–19.0 nJ (18,600–19,000 pJ) per classification, confirming its suitability for integration into wearable and IoMT ECG monitoring nodes. These results demonstrate that co-designed fixed-point Hjorth hardware and quantized DNNs can deliver a favorable trade-off between diagnostic performance, area, power, latency, and per-inference energy compared with existing MCU-, FPGA-, and ASIC-based ECG classifiers. Full article

The Internet of Medical Things (IoMT) is transforming healthcare through continuous patient monitoring, telemedicine, cloud–edge services, and Healthcare 5.0. However, the rapid growth of interconnected medical devices has expanded the healthcare cyberattack surface, making intelligent intrusion detection essential for protecting sensitive medical data and ensuring resilient clinical operations. Existing reviews examine specific aspects of AI-powered intrusion detection but rarely provide a deployment-oriented synthesis linking technical performance with operational and clinical requirements. This review critically examines Artificial Intelligence (AI)-powered Intrusion Detection Systems (IDSs) for IoMT across six analytical dimensions: detection performance, explainability, privacy preservation, computational efficiency, benchmarking practices, and cross-dataset generalization. This structured narrative review adopted the PRISMA 2020 framework to ensure transparent record identification, screening, and reporting, with evidence synthesized qualitatively rather than through quantitative meta-analysis. A total of 5127 records published between 2021 and 2026 were screened, resulting in 24 primary studies supported by 115 complementary studies. The findings show that machine learning, deep learning, hybrid AI, Explainable Artificial Intelligence (XAI), Federated Learning (FL), blockchain-assisted security, and edge intelligence have significantly advanced IoMT intrusion detection. However, despite benchmark accuracies often exceeding 95%, deployment remains constrained by dataset dependency, weak cross-dataset generalization, computational overhead, limited explainability, fragmented benchmarking, and insufficient operational validation. This review identifies deployment readiness, rather than predictive accuracy alone, as the principal challenge for next-generation healthcare cybersecurity and provides a practical framework for developing trustworthy, interoperable, privacy-preserving, and deployment-ready IoMT cybersecurity architectures supported by standardized evaluation protocols. 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: Population growth and rising healthcare demand increasingly strain public healthcare in emerging economies. Further, poor supply visibility, frequent stock-outs, and medicine expiry weaken healthcare supply chain performance (SCP) and patient outcomes. In response, the Internet of Things (IoT) offers promising ways to improve the monitoring, distribution, and management of medical supplies. However, empirical evidence on how technological, organisational, and environmental factors influence IoT adoption and its effect on public healthcare SCP remains limited. This study examined factors influencing IoT adoption and its effect on supply chain performance in public healthcare facilities. Methods: Data were collected from 102 respondents drawn from 90 public healthcare facilities. Results: Factors associated with technological factors have the most significant influence on the adoption of IoT in public healthcare SCs. The lack of significance of organisational and environmental factors may be attributed to the early stage of IoT adoption in public healthcare SCs. Conclusions: This study contributes to the theoretical understanding of IoT adoption by highlighting the dominant role of technological factors over organisational and environmental considerations in resource-constrained public healthcare settings. From a practical perspective, the findings encourage policymakers and healthcare managers to prioritise investments in relevant ICT infrastructure to accelerate IoT adoption. Full article

Telemedicine in emergency and remote care relays medical images from ambulances and rural clinics to a hospital edge-computing server over a congested wireless uplink. Existing work prices such transmissions per bit or per quality-of-experience; neither metric captures the clinical value of a medical transmission. Diagnostic utility vanishes below a modality-specific acceptability floor rather than degrading gracefully, the deadline is determined by triage acuity rather than by the network, and a missed finding is far costlier than a false alarm. A per-bit clearing price therefore disadvantages the node that has expended local compute to produce a compact, diagnostically sufficient stream. We propose SemAuc, a triage-aware truthful mechanism for medical-image admission over a rate-splitting uplink, in which the shared semantic knowledge base rides the common stream, and case-specific residuals ride private streams. SemAuc filters tiers below the diagnostic floor and beyond the clinical deadline, reserves a regulated-price lane for life-threatening cases, and allocates remaining capacity through a single-parameter contestable auction whose bid-independent pre-selection step satisfies the conditions of Myerson’s lemma. The contestable lane is dominant-strategy truthful, individually rational, near-linear in the number of nodes, and achieves a constant-factor density-greedy welfare guarantee; the clinical lanes follow from triage policy without disturbing these properties. Diagnostic value is grounded by an offline kernel fitted on BraTS and CheXpert. On a Rayleigh-faded uplink at two hundred contending nodes, SemAuc preserves the high-acuity diagnostic service-level objective where bit-centric benchmarks fail, and tracks the offline optimum. Full article

With the rapid expansion of the Internet of Medical Things (IoMT), the risks of cybersecurity have increased exponentially in healthcare settings, exposing patients’ safety. Three fundamental issues that existing intrusion detection systems (IDS) are challenged by are: (1) limited cross-domain generalization, (2) high computation requirements not suitable for edge deployment, and (3) absence of systematic comparison between classical machine learning (ML) and deep learning (DL) approaches on IoMT-specific data. In this paper, we propose a multi-dataset evaluation framework that covers six models (Random Forest, XGBoost, DNN, CNN, LSTM, and CNN-LSTM) across three different datasets: WUSTL-EHMS-2020, Edge-IIoTset, and UNSW-NB15. We show that there is a scale-dependent pattern: classical ensemble methods work best when the data is small (F1 = 0.914 ± 0.013 on WUSTL-EHMS-2020); the proposed hybrid CNN–Ensemble framework performs best when the data is large (F1 = 0.968 ± 0.002 on UNSW-NB15 with 62.8% fewer features). The proposed framework achieves a total model size of 2.11 MB and an inference latency of 111.6 ms, with seven out of the top 15 discriminative features being patient vital signs, giving the first quantitative evidence that physiological data systematically contributes to IoMT attack detection, which is demonstrated through an explainability analysis using the SHAP approach. Cross-dataset generalization experiments across six transfer scenarios expose fundamental limitations in domain transfer, establishing an important baseline for future research. Full article

Durable medical devices (DMD) are essential healthcare assets, yet their management in public hospitals is constrained by fragmentation, limited traceability, reactive maintenance, and weak lifecycle integration. This study proposes a framework for digitally enabled, sustainable, and circular DMD management. A mixed-methods design integrated a literature review, expert consultation using the Best–Worst Method, weighted technology nominations, and case-based process mapping in Brazilian hospitals. Eleven experts assessed the criteria guiding Industry 4.0 technology selection for DMD management and the technologies best responding to these priorities; nine consistent judgments were aggregated. Patient-Centered Care, Operational Efficiency, and Resource Efficiency and Cost Reduction emerged as the leading influences on technology selection. Big Data and Analytics, Artificial Intelligence, the Internet of Things, Cloud Computing, Cyber-Physical Systems, Smart Sensors, and Machine Learning formed the priority portfolio, accounting for 84% of the weighted score. The cases contextualized these priorities by revealing discontinuous information flows, limited asset visibility, corrective maintenance, fragmented governance, and weak end-of-life practices. By connecting decision priorities and technological capabilities with observed gaps, the TO-BE framework organizes sustainable procurement, traceable use, predictive maintenance, redeployment, refurbishment, and responsible disposal through material and information flows, providing a pathway for digital and circular transformation in resource-constrained healthcare systems. Full article

The growing demand for wearable and Internet of Medical Things (IoMT) devices is driving the development of compact, low-power platforms for continuous physiological monitoring. Bioimpedance analysis represents a versatile non-invasive technique for the assessment of tissue properties, body composition, and respiratory dynamics. This work presents a comprehensive experimental validation of a compact bioimpedance measurement platform based on the SENSIPLUS chip, a CMOS sensor interface integrating a frequency-programmable lock-in amplifier for Electrochemical Impedance Spectroscopy in the 10 kHz–1 MHz range. The platform was validated at three complementary levels: (i) electrical characterization on Debye tissue-equivalent circuits using a three-point bilinear calibration, with analysis of the electrode–skin contribution and repeatability assessment; (ii) in vivo multi-frequency bioimpedance spectroscopy (BIS) with Cole–Cole model fitting and hook-effect correction; and (iii) single-frequency thoracic impedance plethysmography for respiratory monitoring. Results were compared against an Agilent E4980A precision Inductance (L), Capacitance (C), and Resistance (R) meter and a calibrated spirometer. The presented device achieved a maximum resistance error below 5.7% and reactance deviation under 6 $\mathsf{\Omega }$ across the investigated frequency range, Cole–Cole parameters consistent with reference values, and strong linear correlation ($R^2=0.97$) between thoracic impedance variations and tidal volume, with respiratory rate estimation errors below 2% across the ten sessions, specifically 1.43% during normal breathing and 1.96% during deep breathing. These results demonstrate that the SENSIPLUS-based platform achieves metrological performance compatible with the requirements of wearable IoMT applications, here demonstrated in a single-subject proof-of-concept study, while relying for all critical analog functions on a compact ($1.5\times 1.5$) mm² system-on-chip with low power consumption (1.5 mW). Full article

The Internet of Medical Things (IoMT) is growing quickly, which has greatly increased the cybersecurity attacks on the healthcare systems. Security techniques used for preventing attacks can improve patient safety, which is most important. As most of the datasets generated by the IoMT have high dimensions, feature selection is needed for accurate identification of data, along with deployment in real-time with limited resources. Moreover, the most influential features need to be identified for intrusion detection. Thus, this paper proposes a novel explainable hybrid framework, DeepMedShield-XAI, using the particle swarm optimization (PSO) method for feature selection and classifying the selected features using deep learning algorithms. The highest performing model among the three deep learning models is the deep neural network (DNN) with 99.68% and 99.87% test accuracy on the CICIoMT2024 and IoMT_TrafficData datasets. The findings of explainable artificial intelligence (XAI) methods reveal that the CICIoMT2024 dataset relies on connection-level features like length, protocol type, and TCP flags, while the IoMT_TrafficData dataset uses flow-based attributes like flow length, byte counts, and packet speeds without a single feature dominating across attack types. The proposed DeepMedShield-XAI framework: the results indicate that cyberattacks and unauthorized access attempts can be detected early by DeepMedShield-XAI, which can substantially improve the security of IoMT devices. This drives research on lightweight, explainable, and real-time security frameworks to protect patient data and ensure healthcare system reliability. Full article

Background/Objectives: Amb a 1 is the major allergenic component of Ambrosia artemisiifolia pollen and a clinically relevant marker of genuine ragweed sensitization. Heart failure is increasingly recognized as a systemic syndrome shaped by immune activation, endothelial dysfunction, fibrosis, neurohormonal imbalance, pulmonary comorbidity, and environmental exposures. This narrative review aims to synthesize the translational evidence linking Amb a 1-specific IgE, IgE-mediated inflammation, allergic airway disease, and cardiovascular remodeling in heart failure. Methods: A targeted narrative review was performed, integrating evidence on component-resolved ragweed diagnosis, IgE-FcεRI signaling, mast cell and eosinophil biology, pollen exposure, cardiovascular inflammation, and heart failure pathophysiology. Results: No dedicated clinical studies have validated Amb a 1-specific IgE as a diagnostic, prognostic, or therapeutic biomarker in heart failure. However, adjacent evidence supports biologically plausible links between allergen-specific IgE responses and cardiovascular dysfunction, including mast cell activation, cytokine release, endothelial perturbation, oxidative stress, microvascular dysfunction, pulmonary-cardiac interaction, and myocardial fibrosis. Amb a 1-specific IgE may therefore identify a seasonally vulnerable heart failure phenotype, particularly in patients with allergic rhinitis, asthma, eosinophilic inflammation, or recurrent symptom worsening during ragweed season. A systemic/indirect pathway operating through allergic airway disease is distinguished from a postulated direct cardiac pathway; the latter remains strictly speculative, as no direct evidence demonstrates that inhaled Amb a 1 reaches or activates cardiac mast cells in vivo. Conclusions: Amb a 1-specific IgE should not currently be used to infer cardiac causality or modify heart failure therapy. Prospective, phenotype-rich, exposure-informed studies are needed to determine whether ragweed sensitization has clinically meaningful implications for heart failure endotyping, seasonal risk assessment, and cardio-allergology care. These findings may inform patient-centered heart failure management by improving the interpretation of seasonal dyspnea, allergic comorbidity, and symptom fluctuations in ragweed-endemic regions. Full article