Search Results (204)

The rapid digital transformation of healthcare has improved clinical efficiency, patient engagement, and data accessibility, but it has also introduced significant cyber security and data privacy challenges. Healthcare IT systems increasingly rely on interconnected networks, electronic health records (EHRs), tele-medicine platforms, cloud infrastructures, and Internet of Medical Things (IoMT) devices, which collectively expand the attack surface for cyber threats. This scoping review maps and synthesizes recent evidence on cyber security risks in healthcare, including ransomware, data breaches, insider threats, and vulnerabilities in legacy systems, and examines key data privacy concerns related to patient confidentiality, regulatory compliance, and secure data governance. We also review contemporary security strategies, including encryption, multi-factor authentication, zero-trust architecture, blockchain-based approaches, AI-enabled threat detection, and compliance frameworks such as HIPAA and GDPR. Persistent challenges include integrating robust security with clinical usability, protecting resource-limited hospital environments, and managing human factors such as staff awareness and policy adherence. Overall, the findings suggest that effective healthcare cyber security requires a multi-layered defense combining technical controls, continuous monitoring, governance and regulatory alignment, and sustained organizational commitment to security culture. Future research should prioritize adaptive security models, improved standardization, and privacy-preserving analytics to protect patient data in increasingly complex healthcare ecosystems. Full article

Medical artificial intelligence (AI) systems depend heavily on high-quality data representations to support accurate prediction, diagnosis, and clinical decision-making. However, the availability of large, well-annotated medical datasets is often constrained by cost, privacy concerns, and the need for expert labeling, motivating growing interest in self-supervised representation learning. Among these approaches, contrastive learning has emerged as one of the most influential paradigms, driving major advances in representation learning across computer vision and natural language processing. This paper presents a comprehensive review of contrastive learning in medical AI, highlighting its theoretical foundations, methodological developments, and practical applications in medical imaging, electronic health records, physiological signal analysis, and genomics. Furthermore, we identify recurring challenges, including pair construction, sensitivity to data augmentations, and inconsistencies in evaluation protocols, while discussing emerging trends such as multimodal alignment, federated learning, and privacy-preserving frameworks. Through a synthesis of current developments and open research directions, this review provides insights to advance data-efficient, reliable, and generalizable medical AI systems. Full article

Brain cancer detection is dependent on multiple diagnostic techniques. Histopathological diagnosis, although the most effective, requires the extraction of cancer cells, which is very risky and painful for a patient. Another popular noninvasive image-based diagnosis technique is magnetic resonance imaging (MRI). Brain diagnosis data based on MRI scans are highly sensitive and private. This study proposes a single-to-multimodal transformation technique that generates synthetic histopathological data from expert-labelled brain MRI datasets using transfer learning techniques. Furthermore, to preserve a patient’s privacy, an encryption module is used to encrypt the MRI image data and the respective histopathological notations. The Kruskal–Wallis statistical test is also used to analyze the radiogemomics dataset. The trained module is also encrypted, only to be accessed by authorized medical personnel. The transfer learning modules (CNN-based deep learning model, ViT, Resnet101, and YOLOv8) are used here and achieved $99.60\%$ accuracy. Full article

Background/Objectives: Federated learning (FL) offers a way for healthcare institutions to collaboratively train machine learning models without sharing sensitive patient data. This systematic review aims to comprehensively synthesize the ethical dimensions of FL in healthcare, integrating privacy preservation, algorithmic fairness, governance, and equitable access into a unified analytical framework. The application of FL in healthcare between January 2020 and December 2024 is examined, with a focus on ethical issues such as algorithmic fairness, privacy preservation, governance, and equitable access. Methods: Following PRISMA guidelines, six databases (PubMed, IEEE Xplore, Web of Science, Scopus, ACM Digital Library, and arXiv) were searched. The PROSPERO registration is CRD420251274110. Studies were selected if they described FL implementations in healthcare settings and explicitly discussed ethical considerations. Key data extracted included FL architectures, privacy-preserving mechanisms, such as differential privacy, secure multiparty computation, and encryption, as well as fairness metrics, governance models, and clinical application domains. Results: Out of 3047 records, 38 met the inclusion criteria. The most popular applications were found in medical imaging and electronic health records, especially in radiology and oncology. Through thematic analysis, four key ethical themes emerged: algorithmic fairness, which addresses differences between clients and attributes; privacy protection through formal guarantees and cryptographic techniques; governance models, which emphasize accountability, transparency, and stakeholder engagement; and equitable distribution of computing resources for institutions with limited resources. Considerable variation was observed in how fairness and privacy trade-offs were evaluated, and only a few studies reported real-world clinical deployment. Conclusions: FL has significant potential to promote ethical AI in healthcare, but advancement will require the development of common fairness standards, workable governance plans, and systems to guarantee fair benefit sharing. Future studies should develop standardized fairness metrics, implement multi-stakeholder governance frameworks, and prioritize real-world clinical validation beyond proof-of-concept implementations. Full article

Wireless Body Area Networks (WBANs) enable real-time data collection for medical monitoring, sports tracking, and environmental sensing, driven by Internet of Things advancements. Their layered architecture supports efficient sensing, aggregation, and analysis, but energy constraints from transmission (over 60% of consumption), idle listening, and dynamic conditions like body motion hinder adoption. Challenges include minimizing energy waste while ensuring data reliability, Quality of Service (QoS), and adaptation to channel variations, alongside algorithm complexity and privacy concerns. This paper reviews recent power control mechanisms in WBANs, encompassing feedback control, dynamic and convex optimization, graph theory-based path optimization, game theory, reinforcement learning, deep reinforcement learning, hybrid frameworks, and emerging architectures such as federated learning and cell-free massive MIMO, adopting a systematic review approach with a focus on healthcare and IoT application scenarios. Achieving energy savings ranging from 6% (simple feedback control) to 50% (hybrid frameworks with emerging architectures), depending on method complexity and application scenario, with prolonged network lifetime and improved reliability while preserving QoS requirements in healthcare and IoT applications. Full article

The Internet of Medical Things represents a pivotal application of Internet of Things technology in Healthcare 4.0, offering substantial practical benefits in enhancing medical quality, reducing costs, and minimizing errors. In history, researchers have proposed numerous privacy-preserving authentication schemes to safeguard Internet of Medical Things applications. Nevertheless, due to design shortcomings, existing solutions still encounter significant security and performance challenges, rendering them impractical for real-world use. To resolve the issue, this work introduces a novel practical Internet of Medical Things-based smart healthcare system, leveraging a pairing-free certificateless signature scheme and hash-based message authentication code. Through formal security proofs under standard cryptographic assumptions, and performance analysis, our scheme demonstrates enhanced security while maintaining desirable computational and communication efficiency. Full article

Background/Objective: The integration of machine learning (ML) and deep learning (DL) has significantly enhanced medical image classification, especially in histopathology, by improving diagnostic accuracy and aiding clinical decision making. However, data privacy concerns and restrictions on sharing patient data limit the development of effective DL models. Federated learning (FL) offers a promising solution by enabling collaborative model training across institutions without exposing sensitive data. This systematic review aims to comprehensively evaluate the current state of FL applications in histopathological image classification by identifying prevailing methodologies, datasets, and performance metrics and highlighting existing challenges and future research directions. Methods: Following PRISMA guidelines, 24 studies published between 2020 and 2025 were analyzed. The literature was retrieved from ScienceDirect, IEEE Xplore, MDPI, Springer Nature Link, PubMed, and arXiv. Eligible studies focused on FL-based deep learning models for histopathology image classification with reported performance metrics. Studies unrelated to FL in histopathology or lacking accessible full texts were excluded. Results: The included studies utilized 10 datasets (8 public, 1 private, and 1 unspecified) and reported classification accuracies ranging from 69.37% to 99.72%. FedAvg was the most commonly used aggregation algorithm (14 studies), followed by FedProx, FedDropoutAvg, and custom approaches. Only two studies reported their FL frameworks (Flower and OpenFL). Frequently employed model architectures included VGG, ResNet, DenseNet, and EfficientNet. Performance was typically evaluated using accuracy, precision, recall, and F1-score. Federated learning demonstrates strong potential for privacy-preserving digital pathology applications. However, key challenges remain, including communication overhead, computational demands, and inconsistent reporting standards. Addressing these issues is essential for broader clinical adoption. Conclusions: Future work should prioritize standardized evaluation protocols, efficient aggregation methods, model personalization, robustness, and interpretability, with validation across multi-institutional clinical environments to fully realize the benefits of FL in histopathological image classification. Full article

Digital telecommunications have become the backbone of modern healthcare, transforming how patients and professionals interact, share information, and deliver treatment. The integration of telecommunications with medicine, biomedical engineering and health services has enabled rapid growth in telemedicine, remote patient monitoring, wearable biomedical devices, and data-driven clinical decision-making. Emerging technologies such as artificial intelligence, big data analytics, virtual and augmented reality and robotic tele-surgery are further expanding the scope of digital health. This review provides a comprehensive overview of the role of telecommunications in medicine and biomedical engineering. We classify key applications, highlight enabling technologies and critically examine the challenges regarding interoperability, data security, latency, and cost. Finally, we discuss future directions, including 5G/6G networks, edge computing, and privacy-preserving medical AI, emphasizing the need for reliable and equitable access to telecommunications-enabled healthcare worldwide. Full article

The Health Data Space (HDS) is a promising platform for the secure health data sharing among entities including patients and healthcare providers. However, health data is highly sensitive and critical for diagnosis, and unauthorized access or destruction by malicious users can lead to serious privacy leaks or medical negligence. Thus, robust access control, privacy preservation, and data integrity are essential for HDS. Although Ciphertext-Policy Attribute-Based Encryption (CP-ABE) supports secure sharing, it has limitations when directly applied to HDS. Many current schemes cannot simultaneously handle data integrity violations, trace and revoke malicious users, and protect against privacy leaks from plaintext access policies, with key escrow being another major risk. To overcome these issues, we put forward a Traceable and Revocable Privacy-Preserving Data Sharing (TRPPDS) scheme. Our solution uses a novel distributed CP-ABE with a large universe alongside data auditing to provide fine-grained, key-escrow-resistant access control over unbounded attributes and guarantee data integrity. It also features tracing-then-revocation and full policy hiding to thwart malicious users and protect policy privacy. Formal security analysis is presented for our proposal, with thorough performance assessment also demonstrates its feasibility in HDS. Full article

In the past, health records were primarily on paper and were essential for recording the results of patient information and treatments. The deployment of “electronic health records” (EHRs) is a new development in healthcare that enables authenticated data storage, reliability when accessing data, and the establishment of easy communication centralized across healthcare service providers. This change enhances the quality of operations for medical environment decision-making using clinical data and patient involvement. Nevertheless, ensuring the authenticity of “EHRs” is a challenging task as a result of the weaknesses of centralized systems. We, therefore, suggest the implementation of (ABE), particularly (CP-ABE) using the blockchain technique, to overcome this problem. CP-ABE maintains data confidentiality and accuracy by encrypting access policies and smart contracts, thus allowing authorized users to decrypt information based on predetermined attributes. In this way, EHRs are ensured to be unaltered as patients’ privacy is preserved, and healthcare providers are not allowed to evaluate people records without consent. The machine learning techniques (“SVM, RF and Naïve Bayes”) used with datasets like “Cleveland Heart Disease” explain the cause risk factors for speed diagnosis and for cardiac disorders. Such a system not only fortifies the security of EHRs but also provides healthcare professionals with the necessary tools to improve patient care. The use of state-of-the-art encryption methods together with predictive analytics allows healthcare providers to protect patient privacy and at the same time make healthcare delivery more efficient through the use of a clinically informed final judgment of patient and personalized wellness plans. Full article

Stroke is one of the leading causes of death and long-term disability worldwide, and effective prevention depends on fast, reliable, and privacy-preserving risk assessment. This study proposes a federated IoT-enabled framework that combines feature-optimized machine learning (ML) with real-time patient monitoring to predict and detect brain stroke risk. The system operates in two stages: (i) a stroke prediction module that builds an ML model for risk assessment and (ii) an IoT-based framework that continuously monitors patients and triggers timely alerts. The ML pipeline starts from a clinical–physiological dataset containing 17 initial attributes and applies a feature optimization strategy based on feature importance, selection, and reduction to identify the most informative predictors of stroke. To support multi-center deployment while protecting patient confidentiality, the ML pipeline is embedded within a standard Federated Averaging (FedAvg) architecture, where multiple home or hospital IoT gateways collaboratively train a shared global model without exchanging raw patient data. In each communication round, clients perform local training and the server aggregates client model parameters to update the global model. The resulting federated global model matches the performance of the centralized baseline, achieving 99.44% test accuracy while preserving data locality. Integrated with IoT devices, the system can detect pre-stroke syndromes in real time and automatically notify family members or emergency medical services, making it suitable for both home and hospital environments and offering a practical path toward early intervention and improved stroke outcomes. Full article

The Internet of Medical Things (IoMT) improves healthcare delivery through many medical applications. Because of medical data sensitivity and limited resources of wearable technology, privacy and security are significant challenges. Traditional encryption does not provide secure computation on encrypted data, and many blockchain-based IoMT solutions partially rely on centralized structures. IoMT with dynamic encryption is an innovative privacy-preserving system that combines sensitivity-based classification and advanced encryption to address these issues. The study proposes privacy-preserving IoMT framework that dynamically adapts its cryptographic strategy based on data sensitivity. The proposed approach uses a hybrid SDAIPA (SDAIA-HIPAA) classification model that integrates Saudi Data and Artificial Intelligence Authority (SDAIA) and Health Insurance Portability and Accountability Act (HIPAA) guidelines. This classification directly governs the selection of encryption mechanisms, where Advanced Encryption Standard (AES) is used for low-sensitivity data, and Fully Homomorphic Encryption (FHE) is used for high-sensitivity data. The Whale Optimization Algorithm (WOA) is used to maximize cryptographic entropy of FHE keys and improves security against attacks, resulting in an Optimized FHE that is conditionally used based on SDAIPA outputs. This proposed approach provides a novel scheme to dynamically align cryptographic intensity with data risk and avoids the overhead of uniform FHE use while ensuring strong privacy for critical records. Two datasets are used to assess the proposed approach with up to 806 samples. The results show that the hybrid OHE-WOA outperforms in the percentage of sensitivity of privacy index with dataset 1 by 78.3% and 12.5% and with dataset 2 by 89% and 19.7% compared to AES and RSA, respectively, which ensures its superior ability to preserve privacy. Full article

Artificial Intelligence (AI) plays an important role in driving innovation in smart healthcare by providing accurate, scalable, and privacy-preserving diagnostic options. Pneumonia is still a major global health issue, and early detection is key to improving patient outcomes. This study proposes a federated deep learning (FL) approach for automatic pneumonia detection using chest X-ray images, considering both diagnostic efficacy and data privacy. Two models were developed and tested: a custom-developed convolutional neural network and a VGG16 transfer learning architecture. The framework evaluates diagnostic efficacy in both centralized and federated scenarios, taking into account heterogeneous client distributions and class imbalance. F1-score and accuracy values for the federated models indicate competitive levels, with F1-scores greater than 0.90 for pneumonia, being robust even when the data is not independent and identically distributed. Results confirm that FL could be tested as a privacy-preserving way to manage medical imaging and intelligence across distributed healthcare. This study provides a potential proof of concept of how to incorporate federated AI into smart healthcare and gives direction toward clinically tested and real-world applications. Full article

Breast cancer is the second leading cause of cancer-related mortality among women. Early detection enables timely treatment, improving survival outcomes. This paper presents a comparative evaluation of federated learning (FL) frameworks for multiclass breast cancer classification using ultrasound images drawn from three datasets: BUSI, BUS-UCLM, and BCMID, which include 600, 38, and 323 patients, respectively. Five state-of-the-art networks were tested, with MobileNet, ResNet and InceptionNet identified as the most effective for FL deployment. Two aggregation strategies, FedAvg and FedProx, were assessed under varying levels of data heterogeneity in two and three client settings. Results from experiments indicate that the FL models outperformed local and centralized training, bypassing the adverse impacts of data isolation and domain shift. In the two-client federations, FL achieving up to 8% higher accuracy and almost 6% higher macro-F1 scores on average that local and centralized training. FedProx on MobileNet maintained a stable performance in the three-client federation with best average accuracy of 73.31%, and macro-F1 of 67.3% despite stronger heterogeneity. Consequently, these results suggest that the proposed multiclass model has the potential to support clinical workflows by assisting in automated risk stratification. If deployed, such a system could allow radiologists to prioritize high-risk patients more effectively. The findings emphasize the potential of federated learning as a scalable, privacy-preserving infrastructure for collaborative medical imaging and breast cancer diagnosis. Full article

Background: Early and accurate cancer detection remains a critical challenge in global healthcare. Deep learning has shown strong diagnostic potential, yet widespread adoption is limited by dependence on high-performance hardware, centralized servers, and data-privacy risks. Methods: This study introduces a browser-based multi-cancer classification framework that performs real-time, client-side inference using TensorFlow.js—eliminating the need for external servers or specialized GPUs. The proposed model fine-tunes the Xception architecture, leveraging depthwise separable convolutions for efficient feature extraction, on a large multi-cancer dataset of over 130,000 histopathological and cytological images spanning 26 cancer types. It was benchmarked against VGG16, ResNet50, EfficientNet-B0, and Vision Transformer. Results: The model achieved a Top-1 accuracy of 99.85% and Top-5 accuracy of 100%, surpassing all comparators while maintaining lightweight computational requirements. Grad-CAM visualizations confirmed that predictions were guided by histopathologically relevant regions, reinforcing interpretability and clinical trust. Conclusions: This work represents the first fully browser-deployable, privacy-preserving deep learning framework for multi-cancer diagnosis, demonstrating that high-accuracy AI can be achieved without infrastructure overhead. It establishes a practical pathway for equitable, cost-effective global deployment of medical AI tools. Full article