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• Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling
  BACKGROUND: Artificial intelligence-enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains underexplored. METHODS: A convolutional neural network was …
  openalex Joshua Mayourian, William La Cava, Akhil Vaid, Girish N. Nadkarni 等 2024-02-05 置信度 0.72 MedicineReceiver operating characteristicLeft ventricular hypertrophyInternal medicineCardiology
• Towards Secure and Efficient Farming Using Self-Regulating Heterogeneous Federated Learning in Dynamic Network Conditions
  The advancement of precision agriculture increasingly depends on innovative technological solutions that optimize resource utilization and minimize environmental impact. This paper introduces a novel heterogeneous federated learning architecture specifically d…
  openalex Sai Puppala, Koushik Sinha 2025-04-25 置信度 0.72 AgricultureComputer scienceBusinessComputer networkBiology
• Practical Federated Learning without a Server
  Federated Learning (FL) enables end-user devices to collaboratively train ML models without sharing raw data, thereby preserving data privacy. In FL, a central parameter server coordinates the learning process by iteratively aggregating the trained models rece…
  openalex Akash Dhasade, Anne-Marie Kermarrec, Erick Lavoie, Johan Pouwelse 等 2025-03-30 置信度 0.72 Computer scienceWorld Wide Web
• Intelligent waste sorting for urban sustainability using deep learning
  Smart cities’ have experienced an increasingly higher rate of urbanization and increase of the population leading to strengthening the pressing needs in waste management. In this paper, we present an intelligent waste classification system that utilises Convol…
  openalex G.F. Ahmad, Fizza Muhammad Aleem, Tahir Alyas, Qaiser Abbas 等 2025-07-25 置信度 0.72 SustainabilitySortingComputer scienceUrban sustainabilityDeep learning
• AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning
  Federated learning enables users to collaboratively train a machine learning model over their private datasets. Secure aggregation protocols are employed to mitigate information leakage about the local datasets from user-submitted model updates. This setup, ho…
  openalex Ghada Almashaqbeh, Zahra Ghodsi 2025-03-07 置信度 0.72 AnonymityInternet privacyComputer scienceComputer securityFederated learning
• Deep learning for intrusion detection in emerging technologies: a comprehensive survey and new perspectives
  Abstract Intrusion Detection Systems (IDS) can help cybersecurity analysts detect malicious activities in computational environments. Recently, Deep Learning (DL) methods in IDS have demonstrated notable performance, revealing new underlying cybersecurity patt…
  openalex Euclides Carlos Pinto Neto, Shahrear Iqbal, Scott Buffett, Madeena Sultana 等 2025-08-20 置信度 0.72 Computer scienceIntrusion detection systemDeep learningData scienceEmerging technologies
• Federated learning for the pathogenicity annotation of genetic variants in multi-site clinical settings
  MOTIVATION: Rare diseases collectively affect 5% of the population. However, fewer than 50% of rare disease patients receive a molecular diagnosis after whole genome sequencing. Supervised machine learning is a valuable approach for the pathogenicity scoring o…
  openalex Nigreisy Montalvo, Francisco Requena Silvente, Emidio Capriotti, Antonio Rausell 2025-09-19 置信度 0.72 Genetic variantsAnnotationPathogenicityComputer scienceLicense
• Blockchain-based federated learning framework for malicious node detection in internet of vehicles (IoV) networks using fog and cloud computing
  Due to the continuous digitalization, IoV networks are vulnerable to various communication attacks by malicious network nodes. In these attacks, the malicious entities disseminate faulty information in the network, which affects quick and intelligent decision-…
  openalex Srinivas Reddy Bandarapu, Muhammad Bilal, Pushpalika Chatterjee, Adnan Mustafa Cheema 等 2025-07-21 置信度 0.72 BlockchainCloud computingComputer scienceNode (physics)The Internet
• Realistic Urban Traffic Generator Using Decentralized Federated Learning for the SUMO Simulator
  Realistic urban traffic simulation is essential for sustainable urban planning and the development of intelligent transportation systems. However, generating high-fidelity, time-varying traffic profiles that accurately reflect real-world conditions, especially…
  openalex Alberto Bazán-Guillén, Carlos Beis-Penedo, Diego Cajaraville-Aboy, Pablo Barbecho Bautista 等 2025-01-01 置信度 0.72 Generator (circuit theory)Computer scienceSimulationTransport engineeringEngineering
• A review of security threats and privacy issues in federated learning
  openalex Abhishek Kumar Agrahari, Aarti Gautam Dinker, Rajendra Bahadur Singh 2026-03-02 置信度 0.72 Computer scienceAdversarial systemComputer securityBackdoorFederated learning
• Federated Learning with Adversarial Optimisation for Secure and Efficient 5G Edge Computing Networks
  With the evolution of 5G edge computing networks, privacy-aware applications are gaining significant attention due to their decentralised processing capabilities. However, these networks face substantial challenges to ensure privacy and security, specifically …
  openalex Saniya Zafar, Jonathan White, Phil Legg 2025-09-17 置信度 0.72 Adversarial systemComputer scienceAdversaryRobustness (evolution)Edge computing
• Federated Learning for Privacy-Preserving Apparel Supply Chain Analytics
  The apparel industry operates through highly complex and globalized supply chains, where effective data analytics plays a critical role in improving demand forecasting, inventory management, logistics coordination, and sustainability practices. However, organi…
  openalex Mizanur Rahman, Samsul Haque, S M Arif Al Sany 2025-10-16 置信度 0.72 Supply chainAnalyticsComputer scienceBig dataSustainability
• Federated Learning for Medical Image Analysis: Privacy-Preserving Paradigms and Clinical Challenges
  Federated Learning (FL) has emerged as a transformative paradigm in medical image analysis, addressing the critical challenges of data scarcity and patient privacy. By enabling collaborative model training across decentralized datasets without requiring data s…
  openalex Juntao Hu, Zhengjie Yang, Peng Wang, Guanyi Zhao 等 2025-08-11 置信度 0.72 Computer scienceImage (mathematics)Internet privacyData scienceArtificial intelligence
• Federated Learning for Early Cardiac Anomaly Prediction in Cross-Silo IoMT Environments
  Early detection of cardiovascular anomalies remains critical for proactive patient care, especially within the growing ecosystem of Internet of Medical Things (IoMT) devices. This study explores the application of Federated Learning (FL) to predict early cardi…
  openalex Michael Georgiades, Lakis Christodoulou, Andreas Chari, Kezhi Wang 等 2025-06-09 置信度 0.72 SiloComputer scienceAnomaly (physics)Anomaly detectionArtificial intelligence
• Federated Learning-Based Intrusion Detection Framework for Internet of Things and Edge Computing Backed Critical Infrastructure
  Modern Critical Infrastructure (CI) sectors including Smart Girds operate on the Internet of Things and edge computing paradigm. With the enormous growth of these sectors, there are emerging and escalating cyber threats. Traditional Machine Mearning (ML) appro…
  openalex Ruofei Meng, Awais Aziz Shah, Muhammad Ali Jamshed, Dimitrios P. Pezaros 2024-06-09 置信度 0.72 Computer scienceEdge computingInternet of ThingsCritical infrastructureEnhanced Data Rates for GSM Evolution
• An optimized oversampling-based federated transfer learning approach for rotating machinery cluster fault diagnosis
  Abstract With the development of distributed industrial systems, rotating machinery as the core power and transmission unit of complex distributed industrial systems, its fault diagnosis is very necessary and faces the serious challenge of Non-Independent and …
  openalex Zhao Xu, Liya Yu, Shaobo Li, Chuanjiang Li 等 2025-07-23 置信度 0.72 OversamplingFault (geology)Cluster (spacecraft)Transfer of learningComputer science
• AI-Driven Privacy Shield: A Secure and Privacy-Preserving Federated Learning Framework
  Emerging, highly skilled cyberattacks demand novel and robust techniques for AI-powered privacy preservation. Centralized machine learning models can be compromised with a single point of failure, a data breach, or an adversarial attack. The proposed work pres…
  openalex Yasir M. Abdal 2025-09-07 置信度 0.72 Computer scienceFederated learningAdversarial systemScalabilityEncryption
• Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: A comparative study
  An accurate energy consumption prediction becomes crucial with increasing electric vehicle usage for effective power grid management. This research examined the performance of eleven machine learning models for this purpose: Ridge Regression, Lasso Regression,…
  openalex Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay 等 2025-05-08 置信度 0.72 Computer scienceEnergy consumptionMachine learningEnergy (signal processing)Artificial intelligence
• Privacy-Preserving Generative AI in Healthcare Systems Using Federated Learning Approaches
  The research paper focuses on the mechanism of introducing Federated Learning alongside privacy-saving strategies in Generative Artificial Intelligence to healthcare applications. The study analyses the privacy protection/model accuracy trade-off by implementi…
  openalex Rajesh A. Poojari 2026-02-28 置信度 0.72 Federated learningComputer scienceDifferential privacyGenerative grammarArtificial intelligence
• SplitML: A Unified Privacy-Preserving Architecture for Federated Split-Learning in Heterogeneous Environments
  While Federated Learning (FL) and Split Learning (SL) aim to uphold data confidentiality by localized training, they remain susceptible to adversarial threats such as model poisoning and sophisticated inference attacks. To mitigate these vulnerabilities, we pr…
  openalex Devharsh Trivedi, Aymen Boudguiga, Nesrine Kaaniche, Nikos Triandopoulos 2026-01-07 置信度 0.72 Federated learningInferenceComputer scienceHomomorphic encryptionConfidentiality
• Toward intelligent and resilient microgrids: A survey of machine learning approaches for renewable energy integration
  openalex Darioush Razmi, Peyman Razmi, Oluleke Babayomi, Zhenbin Zhang 2026-04-24 置信度 0.72 Renewable energyComputer scienceArtificial intelligenceMachine learningEnergy (signal processing)
• FedCVG: a two-stage robust federated learning optimization algorithm
  Federated learning provides an effective solution to the data privacy issue in distributed machine learning. However, distributed federated learning systems are inherently susceptible to data poisoning attacks and data heterogeneity. Under conditions of high d…
  openalex Runze Zhang, Yang Zhang, Yating Zhao, Bin Jia 等 2025-05-26 置信度 0.72 Computer scienceStage (stratigraphy)Optimization algorithmAlgorithmArtificial intelligence
• A Survey on Federated Learning in Human Sensing
  Human Sensing, a field that leverages technology to monitor human activities, psycho-physiological states, and interactions with the environment, enhances our understanding of human behavior and drives the development of advanced services that improve overall …
  openalex Mohan Li, Martin Gjoreski, Pietro Barbiero, Gašper Slapničar 等 2025-01-07 置信度 0.72 Computer scienceData scienceHuman–computer interaction
• Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging
  Artificial Intelligence (AI) and deep learning models have revolutionized diagnosis, prognostication, and treatment planning by extracting complex patterns from medical images, enabling more accurate, personalized, and timely clinical decisions. Despite its pr…
  openalex Anh T. Tran, Tal Zeevi, Seyedmehdi Payabvash 2025-04-14 置信度 0.72 Generalizability theoryArtificial intelligenceComputer scienceNeuroimagingRobustness (evolution)
• A systematic literature review on federated cyber-attack detection for edge intelligence: Challenges, approaches, and future directions
  The rapid expansion of Edge Computing (EC) and Internet of Things devices has introduced significant cybersecurity challenges, necessitating advanced and privacy-preserving attack detection strategies. Traditional cyber-attack detection methods and centralized…
  openalex Zeseya Sharmin, Md Palash Uddin, Yong Xiang, Feifei Chen 2026-03-20 置信度 0.72 Computer scienceSystematic reviewEnhanced Data Rates for GSM EvolutionInformation retrievalData science
• Robust aggregation algorithms for federated learning in unreliable network environments
  Federated learning (FL) allows joint model training on distributed devices without losing data locality, but its results are significantly worse in unreliable network systems where packets are dropped, clients fail, resources are heterogeneous, and adversarial…
  openalex Ziyang Zeng, Shiyu Yang, Guanyu Ding 2025-10-30 置信度 0.72 Computer scienceNetwork packetOutlierOverhead (engineering)Resilience (materials science)
• Digital Twins: State of the art theory and practice, challenges, and open research questions
  Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation, optimisation and accurate forecasting. However, the theoretical framework and practical implementations o…
  openalex Angira Sharma, Edward Elson Kosasih, Jie Zhang, Alexandra Brintrup 等 2022-08-08 置信度 0.72 ImplementationComputer scienceField (mathematics)Key (lock)Data science
• Client to Server: Heterogeneous Distribution Knowledge Transfer for Federated Learning
  Federated learning (FL) is an emerging distributed machine learning paradigm that provides privacy guarantees for training robust models on distributed clients. The primary challenge of FL is data heterogeneity,which slows down model convergence and degrades m…
  openalex Rui Zhao, Xiao Yang, Peng Zhi, Zhihe Zhang 等 2025-08-05 置信度 0.72 Computer scienceDistribution (mathematics)Transfer (computing)Transfer of learningKnowledge transfer
• Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach
  Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are impacting the daily lives of everyone with their ubiquitous applications. In recent years, AI researchers and practitioners have introduced principles and guidelines to bu…
  openalex Firas Bayram, Bestoun S. Ahmed 2024-12-18 置信度 0.72 Computer scienceTrustworthinessRobustness (evolution)Artificial intelligenceMachine learning
• DataSHIELD: mitigating disclosure risk in a multi-site federated analysis platform
  Motivation: The validity of epidemiologic findings can be increased using triangulation, i.e. comparison of findings across contexts, and by having sufficiently large amounts of relevant data to analyse. However, access to data is often constrained by practica…
  openalex Demetris Avraam, Rebecca Wilson, Noemi Aguirre Chan, Soumya Banerjee 等 2024-12-26 置信度 0.72 Computer scienceBusinessRisk analysis (engineering)
• A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction
  Intrusion detection systems are essential for securing wireless sensor networks (WSNs) and Internet of Things (IoT) environments against various threats. This study presents a novel hybrid machine learning (ML) model that integrates KMeans-SMOTE (KMS) for data…
  openalex Md. Alamin Talukder, Majdi Khalid, Nasrin Sultana 2025-02-07 置信度 0.72 Dimensionality reductionComputer scienceIntrusion detection systemReduction (mathematics)Machine learning
• Federated learning for privacy-preserving AI in human–robot collaboration for smart manufacturing
  Purpose The study aims to address privacy and security challenges in AI-driven human–robot collaboration (HRC) by developing a privacy-preserving federated learning framework. Traditional centralized AI models expose sensitive manufacturing data to cybersecuri…
  openalex Milad Rahmati 2025-04-21 置信度 0.72 Computer scienceRobotHuman–computer interactionHuman–robot interactionInternet privacy
• ZTID-IoV: Zero-Trust Intrusion Detection in IoV Using Neurosymbolic AI Approach With Federated Meta-Learning
  The rapid growth of the Internet of Vehicles (IoVs) and smart consumer electronics has generated cybersecurity concerns that require an intelligent, adaptable, and privacy-preserving Intrusion Detection System (IDS). This study introduces ZTID-IoV, a novel neu…
  openalex Farhan Ullah, Gautam Srivastava, Leonardo Mostarda, Umar Raza 2025-10-23 置信度 0.72 Computer scienceIntrusion detection systemCluster analysisTransparency (behavior)Transformer
• Using Homomorphic Proxy Re‐Encryption to Enhance Security and Privacy of Federated Learning‐Based Intelligent Connected Vehicles
  Intelligent connected vehicles (ICVs) are one of the fast‐growing directions that plays a significant role in the area of autonomous driving. To realize collaborative computation among ICVs, federated learning (FL) or federated‐based large language model (FedL…
  openalex Yang Bai, Y. S. Rao, Hongyan Wu, Juan Wang 等 2025-01-01 置信度 0.72 Homomorphic encryptionComputer scienceProxy (statistics)Computer securityProxy re-encryption
• Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
  This study presents a lightweight autoencoder-based approach for anomaly detection in digit recognition using federated learning on resource-constrained embedded devices. We implement and evaluate compact autoencoder models on the ESP32-CAM microcontroller, en…
  openalex Anja Tanović, Ivan Mezei 2025-07-30 置信度 0.72 AutoencoderComputer scienceMNIST databaseAnomaly detectionInference
• Breakthroughs in Brain Tumor Detection: Leveraging Deep Learning and Transfer Learning for MRI-Based Classification
  Identifying and classifying brain tumors play a pivotal role in gaining insights into their underlying mechanisms. In contemporary medical practice, the integration of Computer-assisted Diagnosis (CAD) and machine learning, particularly deep learning, has sign…
  openalex Alireza Golkarieh, Sajjad Rezvani Boroujeni, Kiana Kiashemshaki, Maryam Deldadehasl 等 2025-07-31 置信度 0.72 Transfer of learningDeep learningComputer scienceArtificial intelligenceMachine learning
• FedEmerge: An Entropy-Guided Federated Learning Method for Sensor Networks and Edge Intelligence
  Introduction: Federated Learning (FL) is a distributed machine learning paradigm where a global model is collaboratively trained across multiple decentralized clients without exchanging raw data. This is especially important in sensor networks and edge intelli…
  openalex Koffka Khan 2025-06-14 置信度 0.72 Computer scienceEnhanced Data Rates for GSM EvolutionEntropy (arrow of time)Artificial intelligenceData science
• RewardChain: A Blockchain-Based Incentive Mechanism for Federated Learning in Consumer-Centric Internet of Medical Things
  Federated learning is a promising approach that enables collaborative machine learning (ML) in distributed environments, such as the Internet of Medical Things (IoMT) while preserving consumer privacy. It allows multiple consumers to collaboratively train a mo…
  openalex Ahmad A Alsharidah, Devki Nandan Jha, Ellis Solaiman, Bo Wei 等 2025-10-27 置信度 0.72 Computer scienceIncentiveProcess (computing)Federated learningBlockchain
• A Multi-View-Based Federated Learning Approach for Intrusion Detection
  Intrusion detection aims to identify the unauthorized activities within computer networks or systems by classifying events into normal or abnormal categories. As modern scenarios often involve multi-source data, multi-view fusion deep learning methods are empl…
  openalex Jia Yu, Guoqiang Wang, Nianfeng Shi, Raghav Saxena 等 2025-10-24 置信度 0.72 Computer scienceIntrusion detection systemRobustness (evolution)AdaptabilityLeverage (statistics)
• Exploiting explanations for model extraction via knowledge distillation and mitigation with private counterfactuals.
  europepmc 2026 置信度 0.80
• The Social Connectedness Index: A large-scale dataset of social ties across geographic locations.
  europepmc 2026 置信度 0.80
• Neurovictimology and the risks of neurotechnologies and artificial intelligence: a forensic and legal perspective.
  europepmc 2026 置信度 0.80
• AI-driven optimization in cloud computing: a systematic review of cost, resource management, and security.
  europepmc 2026 置信度 0.80
• "In ChatGPT-Powered Virtual Influencers We (Dis)Trust?": The Privacy Paradox and the Double-Edged Sword of Ubiquitous Large Language Model (LLM) Generative AI as a General Purpose Technology (GPT) in a Human-Centered AI Ecosystem.
  europepmc 2026 置信度 0.80
• Harnessing the power of FAIR data to advance sex/gender insights in psychotraumatology.
  europepmc 2026 置信度 0.80
• Research integrity and data ethics in AI-driven integrated healthcare: a critical appraisal.
  europepmc 2026 置信度 0.80
• Machine learning-assisted highly efficient thermal management in function-oriented thermochromic smart windows.
  europepmc 2026 置信度 0.80
• Cross-modal privacy-preserving synthesis and mixture-of-experts ensemble for robust ASD prediction.
  europepmc 2025 置信度 0.80
• Chaotic cosine and logistic map based robust image encryption with dual-stage confusion-diffusion architecture.
  europepmc 2026 置信度 0.80
• Shadow AI in Swedish Health Care: Qualitative Analysis of Physicians' Free-Text Answers.
  europepmc 2026 置信度 0.80
• A privacy preserving synthetic learner dataset for learning analytics in technology enhanced higher education.
  europepmc 2026 置信度 0.80
• The pediatric AI readiness framework: bridging evidence to practice in pediatric artificial intelligence.
  europepmc 2026 置信度 0.80
• Limitations of current copyright frameworks for large language models trained on scientific literature.
  europepmc 2026 置信度 0.80
• AI Applications Integrating Legal and Regulatory Perspectives in Mental Health: Systematic Review.
  europepmc 2026 置信度 0.80
• Blockchain-Based Dynamic and Revocable Consent for Secondary Health Data Use: Systematic Review.
  Background The secondary use of health data holds substantial potential for advancing biomedical research, strengthening population health analytics, and enabling artificial intelligence-driven decision-making support. Yet, ensuring that such reuse respects pa…
  europepmc 2026 置信度 0.80
• Slow Harm After Dobbs v. Jackson Women's Health Organization: An Analytic Scoping Review of How Data and Surveillance Infrastructures Reshape Emergency Obstetric Care.
  europepmc 2026 置信度 0.80
• Editorial: Multimodal brain data integration and computational modeling.
  europepmc 2026 置信度 0.80
• SYNNER synthetic data generator framework.
  europepmc 2026 置信度 0.80
• The 2020 US Decennial Census is more private than you (might) think.
  europepmc 2025 置信度 0.80
• Contextualizing the privacy paradox-a risk-benefit analysis of generation z's adoption intentions toward AI-based virtual try-on.
  europepmc 2026 置信度 0.80
• Privacy-Aware Synthetic Tabular Data Generation for Healthcare: Application to Sepsis Detection.
  europepmc 2026 置信度 0.80
• Generative Artificial Intelligence and Large Language Models in Clinical Oncology.
  europepmc 2026 置信度 0.80
• Deep reinforcement learning-based reversible medical image encryption framework for secure IoMT environments.
  The Internet of Medical Things (IoMT) environments face significant challenges in securely transmitting and storing medical images due to limited computational resources, multiple device types, and increasing cybersecurity threats. This paper describes a rever…
  europepmc 2026 置信度 0.80
• Paradoxical Pyogenic Granuloma Associated With Ramucirumab but Not Bevacizumab: A Case Suggesting Differential Effects of VEGF Receptor and Ligand Inhibition.
  europepmc 2026 置信度 0.80
• Artificial intelligence-driven gastrointestinal functional assessment: multimodal imaging, digital biomarkers, and real-time monitoring.
  europepmc 2026 置信度 0.80
• Dysregulated circRNA Expression in Juvenile Myoclonic Epilepsy.
  europepmc 2026 置信度 0.80
• Lymph-node-first Kawasaki disease obscured by deep neck infection in an infant.
  europepmc 2026 置信度 0.80
• The Impact of AI-Recommended Content Affordances on Post-Purchase Intention in Stockout Substitution Scenarios.
  europepmc 2025 置信度 0.80
• Digital Literacy and Interpersonal Trust as Predictors of Willingness to Share Patient-Generated Health Data Among Korean Internet Users: Cross-Sectional Study Using Privacy Calculus and Communication Privacy Management Theories.
  europepmc 2026 置信度 0.80
• Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication.
  europepmc 2026 置信度 0.80
• Inclusive Initiatives in Health Professions Education: A Journal-Led Peer Reviewer Development Initiative.
  europepmc 2026 置信度 0.80
• Ubiquitous Non-Wearable Sensor for Human Sedentary Behavior Monitoring and Characterization.
  europepmc 2026 置信度 0.80
• Adult Occipital Dermoid Cyst With the Initial Manifestation of Subcutaneous Lump: A Case Report.
  europepmc 2026 置信度 0.80
• Algorithm-assisted interpretation of cyclic and differential pulse voltammetry for cardiac troponin detection.
  europepmc 2026 置信度 0.80
• Laryngeal Sarcoidosis: A Young Patient With Aggravating Dyspnea and Cervical Lymphadenopathy.
  europepmc 2026 置信度 0.80
• Barriers and facilitators to using self-sampled tests for the human papillomavirus (HPV): a mixed-methods study to inform a horizon scan.
  europepmc 2026 置信度 0.80
• Human Presence in Short-Form Video Advertising: Social Judgments of Human and AI Presenters Under Privacy Concerns.
  europepmc 2026 置信度 0.80
• Strategic cyber intelligence with advanced analytics in Latin America: a perspective.
  europepmc 2026 置信度 0.80
• Integrating Genetic Information into the Electronic Health Record: The Case of Adolescents' Revelation of Misattributed Parentage.
  europepmc 2026 置信度 0.80
• Contributions of Artificial Intelligence to Decision Making in Nursing: A Scoping Review.
  europepmc 2026 置信度 0.80
• The intelligent neonatal healthcare: a systematic review of machine learning architectures integrating the internet of medical things and blockchain.
  europepmc 2026 置信度 0.80
• Primary leiomyosarcoma of the axilla in a 70-year-old female: a case report of multidisciplinary management from a resource-limited setting.
  europepmc 2026 置信度 0.80
• Outfitting the quest for spatial spread of infections: A review of mobility datasets for population health modelling.
  europepmc 2026 置信度 0.80
• Changing Gender Relations in Canada: The Rise of Gender-Neutral Forenames.
  europepmc 2026 置信度 0.80
• Responsible use of large language models in gastroenterology and hepatology.
  europepmc 2026 置信度 0.80
• Evaluating privacy leakages in LLM-driven ambient clinical documentation.
  europepmc 2026 置信度 0.80
• Unraveling Fibrous Tonsil Lesions: A Case Series on Diagnostic Dilemmas in Pediatric Tonsillectomy.
  europepmc 2026 置信度 0.80
• Beyond the Score: Bias Investigations to Improve the Fairness of Board Certification Exams.
  europepmc 2026 置信度 0.80
• Unmasking the Unseen: Dupilumab-Induced Sarcoidosis in a Young Patient-A Case Report.
  europepmc 2026 置信度 0.80
• Wandering Lymphoma: A Case of Wandering Spleen Secondary to Splenic Marginal Zone Lymphoma.
  europepmc 2026 置信度 0.80
• Leveraging chatbots for enhanced decision-making: a comprehensive literature review.
  europepmc 2026 置信度 0.80
• Evaluation of the Diagnostic Capabilities of Artificial Intelligence (GPT-4) in a Cardiology Department in Sub-Saharan Africa: Cross-Sectional Study.
  europepmc 2026 置信度 0.80
• Blockchain Technology to Enhance Clinical Data Management in Healthcare: A Systematic Literature Review.
  europepmc 2026 置信度 0.80
• Realising the digital twin: a thematic review and analysis of the ethical, legal, and social issues for digital twins in healthcare.
  europepmc 2026 置信度 0.80
• Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.
  europepmc 2026 置信度 0.80
• Privacy rights and improving knowledge are not hierarchical needs: data protection and good epidemiologic standard (DP_GOES) checklist for retrospective observational studies using secondary data.
  europepmc 2026 置信度 0.80
• Desiderata for a biomedical knowledge network: opportunities, challenges and future directions.
  europepmc 2026 置信度 0.80
• Private Ensembles, Public Confidence: A PATE-to-MedPrompt System for Autism Detection.
  europepmc 2026 置信度 0.80
• The history of state preemption and medical device regulation: lessons for artificial intelligence oversight.
  europepmc 2026 置信度 0.80
• An Infant With Hyper IgE Syndrome Mimicking Acute Leukemia.
  europepmc 2026 置信度 0.80