隐私计算与数据要素 · 全部实体（第 36 / 100 页）

本页收录第 3501–3600 条，共 10000 条 · 返回 隐私计算与数据要素 首页

• Utilizing Federated Health Databases and AI-Enhanced Neurodevelopmental Trajectory Mapping for Early Diagnosis of Autism Spectrum Disorder: A Review of Scalable Computational Models
  Early diagnosis of Autism Spectrum Disorder (ASD) remains a critical challenge in pediatric neurodevelopmental care, with significant implications for intervention effectiveness and lifelong outcomes. Traditional diagnostic methods often rely on subjective beh…
  datacite Omolayo, Olasehinde, Okare, Babawale Patrick, Taiwo, Ajao Ebenezer, Aduloju, Tope David 2025 置信度 0.66 Autism Spectrum Disorder (ASD); Federated Health Databases; Neurodevelopmental Trajectory Mapping; Scalable AI Models; Early Diagnosis
• Utilizing Federated Health Databases and AI-Enhanced Neurodevelopmental Trajectory Mapping for Early Diagnosis of Autism Spectrum Disorder: A Review of Scalable Computational Models
  Early diagnosis of Autism Spectrum Disorder (ASD) remains a critical challenge in pediatric neurodevelopmental care, with significant implications for intervention effectiveness and lifelong outcomes. Traditional diagnostic methods often rely on subjective beh…
  datacite Omolayo, Olasehinde, Okare, Babawale Patrick, Taiwo, Ajao Ebenezer, Aduloju, Tope David 2025 置信度 0.66 Autism Spectrum Disorder (ASD); Federated Health Databases; Neurodevelopmental Trajectory Mapping; Scalable AI Models; Early Diagnosis
• Artificial Intelligence-Driven Pharmacovigilance: Integrating Real-World Evidence for Next-Generation Drug Safety
  Pharmacovigilance has evolved from a predominantly manual process of adverse drug reaction (ADR) monitoring to a data-driven discipline that increasingly relies on artificial intelligence (AI) and real-world evidence (RWE). The growing volume of safety reports…
  datacite Yash Kothikar1*, Tanveer Patel2 2026 置信度 0.66 Artificial intelligence; Pharmacovigilance; Real-world evidence; Drug safety; Machine learning; Signal detection
• Artificial Intelligence-Driven Pharmacovigilance: Integrating Real-World Evidence for Next-Generation Drug Safety
  Pharmacovigilance has evolved from a predominantly manual process of adverse drug reaction (ADR) monitoring to a data-driven discipline that increasingly relies on artificial intelligence (AI) and real-world evidence (RWE). The growing volume of safety reports…
  datacite Yash Kothikar1*, Tanveer Patel2 2026 置信度 0.66 Artificial intelligence; Pharmacovigilance; Real-world evidence; Drug safety; Machine learning; Signal detection
• Federated Learning Communication Efficiency in Code Generation Across Model Scales and Client Heterogeneity
  This report synthesises findings from 8 peer-reviewed papers addressing the following research question: How does communication efficiency in federated learning for code generation models scale with model size and client heterogeneity relative to centralized d…
  datacite Assignee Research 2026 置信度 0.66 communicationefficiencyfederatedlearningcode
• Federated Learning Communication Efficiency in Code Generation Across Model Scales and Client Heterogeneity
  This report synthesises findings from 8 peer-reviewed papers addressing the following research question: How does communication efficiency in federated learning for code generation models scale with model size and client heterogeneity relative to centralized d…
  datacite Assignee Research 2026 置信度 0.66 communicationefficiencyfederatedlearningcode
• edithatogo/voiage: v0.2.1
  What's Changed Feature/basic voi methods by @edithatogo in https://github.com/edithatogo/voiage/pull/1 Refactor/object oriented api by @edithatogo in https://github.com/edithatogo/voiage/pull/5 Add development plan and fix compatibility wrappers by @edithatogo…
  datacite Dylan Mordaunt 2026 置信度 0.66
• Supply Chain Fraud Risk Mitigation Using Federated AI Models for Continuous Transaction Integrity Verification
  Supply chain networks are increasingly vulnerable to sophisticated fraud tactics that compromise transactional integrity and threaten organizational resilience. Traditional centralized fraud detection systems struggle to scale across decentralized logistics an…
  datacite Essien, Iboro Akpan, Ajayi, Joshua Oluwagbenga, Erigha, Eseoghene Daniel, Obuse, Ehimah 等 2023 置信度 0.66 Federated AI; Supply Chain Fraud; Transaction Integrity; Distributed Learning; Risk Mitigation
• Supply Chain Fraud Risk Mitigation Using Federated AI Models for Continuous Transaction Integrity Verification
  Supply chain networks are increasingly vulnerable to sophisticated fraud tactics that compromise transactional integrity and threaten organizational resilience. Traditional centralized fraud detection systems struggle to scale across decentralized logistics an…
  datacite Essien, Iboro Akpan, Ajayi, Joshua Oluwagbenga, Erigha, Eseoghene Daniel, Obuse, Ehimah 等 2023 置信度 0.66 Federated AI; Supply Chain Fraud; Transaction Integrity; Distributed Learning; Risk Mitigation
• Developing AI-Augmented Intrusion Detection Systems for Cloud-Based Financial Platforms with Real-Time Risk Analysis
  Cloud-based financial platforms are increasingly targeted by sophisticated cyber threats due to the high-value transactions and sensitive data they manage. Traditional intrusion detection systems (IDS) often struggle to provide timely and accurate threat detec…
  datacite Ayanbode, Noah, Cadet, Emmanuel, Etim, Edima David, Essien, Iboro Akpan 等 2023 置信度 0.66 AI-Augmented Intrusion DetectionCloud-Based Financial PlatformsReal-Time Risk AnalysisAnomaly DetectionExplainable Artificial Intelligence (XAI)
• Developing AI-Augmented Intrusion Detection Systems for Cloud-Based Financial Platforms with Real-Time Risk Analysis
  Cloud-based financial platforms are increasingly targeted by sophisticated cyber threats due to the high-value transactions and sensitive data they manage. Traditional intrusion detection systems (IDS) often struggle to provide timely and accurate threat detec…
  datacite Ayanbode, Noah, Cadet, Emmanuel, Etim, Edima David, Essien, Iboro Akpan 等 2023 置信度 0.66 AI-Augmented Intrusion DetectionCloud-Based Financial PlatformsReal-Time Risk AnalysisAnomaly DetectionExplainable Artificial Intelligence (XAI)
• Multimodal Artificial Intelligence in Oncology: Integrating Radiomics, Pathomics, and Genomics
  The emergence of artificial intelligence (AI) has fundamentally transformed modern oncology by enabling computational interpretation of increasingly complex biomedical datasets derived from radiology, digital pathology, genomics, transcriptomics, and electroni…
  datacite Dr. Isabella Moore* 2026 置信度 0.66 Multimodal artificial intelligence; oncology; radiomics; pathomics; genomics; precision medicine; deep learning; transformer models; explainable AI; multi-omics integration
• Multimodal Artificial Intelligence in Oncology: Integrating Radiomics, Pathomics, and Genomics
  The emergence of artificial intelligence (AI) has fundamentally transformed modern oncology by enabling computational interpretation of increasingly complex biomedical datasets derived from radiology, digital pathology, genomics, transcriptomics, and electroni…
  datacite Dr. Isabella Moore* 2026 置信度 0.66 Multimodal artificial intelligence; oncology; radiomics; pathomics; genomics; precision medicine; deep learning; transformer models; explainable AI; multi-omics integration
• Federated Learning Approaches for Privacy-Preserving Threat Detection in Smart Home IoT Environments
  Smart home Internet of Things (IoT) environments have become increasingly pervasive, offering convenience and automation while simultaneously introducing new cybersecurity vulnerabilities. Traditional centralized machine learning approaches for threat detectio…
  datacite Idika, Chima Nwankwo, Salami, Edward Oziegbe 2025 置信度 0.66 Federated Learning; Smart Home IoT; Privacy-Preserving Threat Detection; Cybersecurity; Edge Computing
• Federated Learning Approaches for Privacy-Preserving Threat Detection in Smart Home IoT Environments
  Smart home Internet of Things (IoT) environments have become increasingly pervasive, offering convenience and automation while simultaneously introducing new cybersecurity vulnerabilities. Traditional centralized machine learning approaches for threat detectio…
  datacite Idika, Chima Nwankwo, Salami, Edward Oziegbe 2025 置信度 0.66 Federated Learning; Smart Home IoT; Privacy-Preserving Threat Detection; Cybersecurity; Edge Computing
• A Comprehensive Review on Disease Detection and Interpretation of Biomedical Imaging Using AI
  The introduction of Artificial Intelligence (AI) in medical imaging is considered to be one of the most important processing advances in current clinical diagnostics. This comprehensive review article presents a timely summary of the use of AI paradigms, namel…
  datacite Siddique, Mohd Arif, Rajput, Ayan 2025 置信度 0.66 Biomedical Imaging; Clinical Decision Support; U-Net; Disease Detection; Convolutional Neural Network; CADx; Medical Image Segmentation; Deep Learning
• A Comprehensive Review on Disease Detection and Interpretation of Biomedical Imaging Using AI
  The introduction of Artificial Intelligence (AI) in medical imaging is considered to be one of the most important processing advances in current clinical diagnostics. This comprehensive review article presents a timely summary of the use of AI paradigms, namel…
  datacite Siddique, Mohd Arif, Rajput, Ayan 2025 置信度 0.66 Biomedical Imaging; Clinical Decision Support; U-Net; Disease Detection; Convolutional Neural Network; CADx; Medical Image Segmentation; Deep Learning
• Deep Learning–Based Soybean Leaf Disease Classification : A Comprehensive Review
  Soybean is one of the most economically important crops worldwide, yet its productivity is severely affected by a wide range of foliar diseases. Traditional disease diagnosis relies on expert visual inspection, which is time-consuming, subjective, and impracti…
  datacite Rathod, Drashti R, R, Padiya Swity, Patel, Ketan 2026 置信度 0.66 Soybean Leaf Disease; Deep Learning; Image Classification; Computer Vision; Smart Agriculture
• Deep Learning–Based Soybean Leaf Disease Classification : A Comprehensive Review
  Soybean is one of the most economically important crops worldwide, yet its productivity is severely affected by a wide range of foliar diseases. Traditional disease diagnosis relies on expert visual inspection, which is time-consuming, subjective, and impracti…
  datacite Rathod, Drashti R, R, Padiya Swity, Patel, Ketan 2026 置信度 0.66 Soybean Leaf Disease; Deep Learning; Image Classification; Computer Vision; Smart Agriculture
• AI-Driven Adaptive Security Frameworks for Decentralized Edge Computing in IoT Ecosystems
  The rapid proliferation of Internet of Things (IoT) devices and the growing adoption of decentralized edge computing architectures have significantly transformed data processing, latency optimization, and real-time decision-making across smart ecosystems. Howe…
  datacite Emeka, Desire, Emmanuel, Igba 2026 置信度 0.66 AI-driven security; Edge computing; Internet of Things; Adaptive defense mechanisms; Trust management; Privacy-preserving model sharing
• AI-Driven Adaptive Security Frameworks for Decentralized Edge Computing in IoT Ecosystems
  The rapid proliferation of Internet of Things (IoT) devices and the growing adoption of decentralized edge computing architectures have significantly transformed data processing, latency optimization, and real-time decision-making across smart ecosystems. Howe…
  datacite Emeka, Desire, Emmanuel, Igba 2026 置信度 0.66 AI-driven security; Edge computing; Internet of Things; Adaptive defense mechanisms; Trust management; Privacy-preserving model sharing
• Artificial Intelligence in Predictive Healthcare: A Systematic Review.
  Background/Objectives: Today, Artificial intelligence (AI) and machine learning (ML) significantly enhance predictive analytics in the healthcare landscape, enabling timely and accurate predictions that lead to proactive interventions, personalized treatment p…
  pubmed Al-Nafjan A, Aljuhani A, Alshebel A, Alharbi A 等 2025 Sep 24 置信度 0.82
• Machine Learning for Structural Health Monitoring of Aerospace Structures: A Review.
  Structural health monitoring (SHM) plays a critical role in ensuring the safety and performance of aerospace structures throughout their lifecycle. As aircraft and spacecraft systems grow in complexity, the integration of machine learning (ML) into SHM framewo…
  pubmed Scarselli G, Nicassio F 2025 Oct 4 置信度 0.82
• A Systematic Literature Review on the Implementation and Challenges of Zero Trust Architecture Across Domains.
  The Zero Trust Architecture (ZTA) model has emerged as a foundational cybersecurity paradigm that eliminates implicit trust and enforces continuous verification across users, devices, and networks. This study presents a systematic literature review of 74 peer-…
  pubmed Mushtaq S, Mohsin M, Mushtaq MM 2025 置信度 0.82
• Artificial intelligence in heart failure - a comprehensive literature review.
  Heart failure (HF) is a leading cause of hospitalization and mortality worldwide, presenting significant management challenges due to its heterogeneity and frequent comorbidities. Despite advancements in treatment, heart failure poses significant burdens on he…
  pubmed Alyacoub R 2025 置信度 0.82
• Unmasking digital deceptions: An integrative review of deepfake detection, multimedia forensics, and cybersecurity challenges.
  Deepfakes, which are driven by developments in generative AI, seriously jeopardize public trust, cybersecurity, and the veracity of information. This study offers a comprehensive analysis of the most recent methods for creating and detecting deepfakes in image…
  pubmed Singh S, Dhumane A 2025 Dec 置信度 0.82
• Fine-tuning large language models in federated learning with fairness-aware prompt selection.
  Large language models (LLMs) require domain-specific fine-tuning for real-world deployment, yet face critical barriers of data privacy and computational constraints. Federated learning (FL) provides an indispensable solution by enabling collaborative tuning ac…
  pubmed Jiang Y, Li Z, Song B 2026 Feb 置信度 0.82
• Advances in medical image segmentation: A comprehensive survey with a focus on lumbar spine applications.
  Medical Image Segmentation (MIS) stands as a cornerstone in medical image analysis, playing a pivotal role in precise diagnostics, treatment planning, and monitoring of various medical conditions. This paper presents a comprehensive and systematic survey of MI…
  pubmed Kabil A, Khoriba G, Yousef M, Rashed EA 2025 Nov 置信度 0.82
• Regulatory Adoption of AI, ML, Computational Modeling & Simulation in In-Silico Clinical Trials for Medical Devices: A Systematic Review.
  This study explores the revolutionary potential of in-silico clinical trials (ISCTs) in medical device development, emphasizing the integration of computational modeling and simulation (CM&S), artificial intelligence (AI), and machine learning (ML). It eva…
  pubmed De A, Lohani A 2026 Jan 置信度 0.82
• A comprehensive overview: deep learning approaches to central serous chorioretinopathy diagnosis.
  To synthesize evidence on deep learning applications for diagnosing central serous chorioretinopathy (CSCR), a macular disorder associated with vision loss, this systematic review categorized studies by diagnostic task and imaging modality. The study evaluates…
  pubmed Shojaeinia M, Hosseini A, Naderi M, Baloutch B 等 2025 Oct 6 置信度 0.82
• How can artificial intelligence transform the training of medical students and physicians?
  Advances in artificial intelligence (AI), particularly generative AI, hold promise for transforming medical education and physician training in response to increasing health-care demands and shortages in the global health-care workforce. Meanwhile, challenges …
  pubmed Ning Y, Ong JCL, Cheng H, Wang H 等 2025 Oct 置信度 0.82
• Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.
  Machine learning (ML) has played a crucial role in advancing precision immunotherapy by integrating multi-omics data to identify biomarkers and predict therapeutic responses. However, a prevalent methodological flaw persists in immunological studies-an overrel…
  pubmed Wang JW, Meng M, Dai MW, Liang P 等 2025 置信度 0.82
• Artificial intelligence in carotid computed tomography angiography plaque detection: Decade of progress and future perspectives.
  The application of artificial intelligence (AI) in carotid atherosclerotic plaque detection via computed tomography angiography (CTA) has significantly advanced over the past decade. This mini-review consolidates recent innovations in deep learning architectur…
  pubmed Wang DY, Yang T, Zhang CT, Zhan PC 等 2025 Sep 28 置信度 0.82
• Integrating big data and artificial intelligence to predict progression in multiple sclerosis: challenges and the path forward.
  Multiple sclerosis (MS) remains a complex and costly neurological condition characterised by progressive disability, making early detection and accurate prognosis of disease progression imperative. While artificial intelligence (AI) combined with big data prom…
  pubmed Khan H, Aerts S, Vermeulen I, Woodruff HC 等 2025 Sep 29 置信度 0.82
• Federated learning for early severity prediction in acute pancreatitis: a multi-center study.
  BACKGROUND: Acute pancreatitis (AP) is a common gastrointestinal disease prone to severe systemic complications, presenting with abdominal pain, nausea, and vomiting. Diagnosis depends on serum markers and imaging. Categorized by the Atlanta classification, ab…
  pubmed Shen Y, Li Z, Li S, Zhou L 等 2025 Sep 29 置信度 0.82
• Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.
  Multi-target drug discovery has become an essential strategy for treating complex diseases involving multiple molecular pathways. Traditional single-target approaches often fall short in addressing the multifactorial nature of conditions such as cancer and neu…
  pubmed Bi X, Wang Y, Wang J, Liu C 2025 Sep 12 置信度 0.82
• Artificial Intelligence and Advanced Digital Health for Hypertension: Evolving Tools for Precision Cardiovascular Care.
  Background : Hypertension remains the leading global risk factor for cardiovascular morbidity and mortality, with suboptimal control rates despite guideline-directed therapies. Digital health and artificial intelligence (AI) technologies offer novel approaches…
  pubmed Skalidis I, Maurizi N, Salihu A, Fournier S 等 2025 Sep 4 置信度 0.82
• Augmented Decisions: AI-Enhanced Accuracy in Glaucoma Diagnosis and Treatment.
  Glaucoma remains a leading cause of irreversible blindness. We reviewed more than 150 peer-reviewed studies (January 2019-July 2025) that applied artificial or augmented intelligence (AI/AuI) to glaucoma care. Deep learning systems analyzing fundus photographs…
  pubmed Zeppieri M, Gagliano C, Tognetto D, Musa M 等 2025 Sep 16 置信度 0.82
• Sustainability of AI-Assisted Mental Health Intervention: A Review of the Literature from 2020-2025.
  This systematic review examines the role of artificial intelligence (AI) in the development of sustainable mental health interventions through a comprehensive analysis of literature published between 2020 and 2025. In accordance with the PRISMA guidelines, 62 …
  pubmed Espino Carrasco DK, Palomino Alcántara MDR, Arbulú Pérez Vargas CG, Santa Cruz Espino BM 等 2025 Sep 4 置信度 0.82
• Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care.
  The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk s…
  pubmed Kumar R, Dougherty C, Sporn K, Khanna A 等 2025 Sep 9 置信度 0.82
• AI in Dentistry: Innovations, Ethical Considerations, and Integration Barriers.
  Artificial Intelligence (AI) is improving dentistry through increased accuracy in diagnostics, planning, and workflow automation. AI tools, including machine learning (ML) and deep learning (DL), are being adopted in oral medicine to improve patient care, effi…
  pubmed Liu TY, Lee KH, Mukundan A, Karmakar R 等 2025 Aug 29 置信度 0.82
• The Expanding Frontier: The Role of Artificial Intelligence in Pediatric Neuroradiology.
  Artificial intelligence (AI) is revolutionarily shaping the entire landscape of medicine and particularly the privileged field of radiology, since it produces a significant amount of data, namely, images. Currently, AI implementation in radiology is continuous…
  pubmed Guarnera A, Napolitano A, Liporace F, Marconi F 等 2025 Aug 27 置信度 0.82
• Federated learning for lesion segmentation in multiple sclerosis: a real-world multi-center feasibility study.
  Multiple sclerosis (MS) is a chronic neuroinflammatory disease driven by immune-mediated central nervous system damage, often leading to progressive disability. Accurate segmentation of MS lesions on MRI is crucial for monitoring disease and treatment efficacy…
  pubmed Hindawi S, Szubstarski B, Boernert E, Tackenberg B 等 2025 置信度 0.82
• Data sharing for responsible artificial intelligence in dentistry: a narrative review of legal frameworks and privacy-preserving techniques.
  Data sharing is essential for ensuring research reproducibility and for developing generalizable artificial intelligence (AI) systems, but it demands robust safeguards for patient privacy. This narrative review aims to guide dental clinicians and researchers i…
  pubmed Brinz J, Eslamiamirabadi N, Salamati A, Tresp V 等 2025 Dec 置信度 0.82
• A survey for large language models in biomedicine.
  Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a compreh…
  pubmed Wang C, Li M, He J, Wang Z 等 2025 Dec 置信度 0.82
• Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.
  The integration of artificial intelligence (AI) into cardiovascular imaging and radiology offers the potential to enhance diagnostic accuracy, streamline workflows, and personalize patient care. However, the rapid adoption of AI has introduced complex ethical …
  pubmed Mehrtabar S, Marey A, Desai A, Saad AM 等 2026 Jun 置信度 0.82
• Teleophthalmology-enabled devices: bridging the gap in rural eye care.
  Rural populations bear a disproportionate burden of preventable vision loss due to scant ophthalmic resources, long travel distances, and delayed diagnoses of vision-threatening conditions such as diabetic retinopathy, glaucoma, and age-related macular degener…
  pubmed Gurnani B, Kaur K 2025 Nov 置信度 0.82
• Federated learning: a privacy-preserving approach to data-centric regulatory cooperation.
  Regulatory agencies aim to ensure the safety and efficacy of medical products but often face legal and privacy concerns that hinder collaboration at the data level. In this paper, we propose federated learning as an innovative method to enhance data-centric co…
  pubmed Horst A, Loustalot P, Yoganathan S, Li T 等 2025 置信度 0.82
• Indoor Abnormal Behavior Detection for the Elderly: A Review.
  Due to the increased age of the global population, the proportion of the elderly population continues to rise. The safety of the elderly living alone is becoming an increasingly prominent area of concern. They often miss timely treatment due to undetected fall…
  pubmed Gu T, Tang M 2025 May 24 置信度 0.82
• Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey.
  The Internet of Things (IoT) is revolutionizing industries by enabling seamless interconnectivity across domains such as healthcare, smart cities, the Industrial Internet of Things (IIoT), and the Internet of Vehicles (IoV). However, IoT security remains a sig…
  pubmed Alfahaid A, Alalwany E, Almars AM, Alharbi F 等 2025 May 26 置信度 0.82
• A Comprehensive Review of Predictive Precision in Scar Medicine: From Molecular Predictors to Machine Learning Models.
  Scars-including keloids, hypertrophic scars, and acne scars-pose substantial functional and psychosocial burdens that current empirical treatments often address by trial-and-error. Quantitative evidence now supports a precision framework. Validated clinical to…
  pubmed Su J, Chen J, Wang T, Lin T 2025 置信度 0.82
• A comprehensive review of techniques, algorithms, advancements, challenges, and clinical applications of multi-modal medical image fusion for improved diagnosis.
  Multi-modal medical image fusion (MMIF) is increasingly recognized as an essential technique for enhancing diagnostic precision and facilitating effective clinical decision-making within computer-aided diagnosis systems. MMIF combines data from X-ray, MRI, CT,…
  pubmed Zubair M, Hussain M, Albashrawi MA, Bendechache M 等 2025 Dec 置信度 0.82
• A machine learning approach for automating review of a RxNorm medication mapping pipeline output.
  Medication mapping to standardized terminologies is an important prerequisite for performing analytics on a federated EHR network. TriNetX LLC operates the largest such network in the world.
  pubmed Hüser M, Doole J, Pinho V, Rouhizadeh H 等 2025 Oct 置信度 0.82
• AI-Powered Building Ecosystems: A Narrative Mapping Review on the Integration of Digital Twins and LLMs for Proactive Comfort, IEQ, and Energy Management.
  Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber-physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performan…
  pubmed Amangeldy B, Tasmurzayev N, Imankulov T, Baigarayeva Z 等 2025 Aug 24 置信度 0.82
• Intention Prediction for Active Upper-Limb Exoskeletons in Industrial Applications: A Systematic Literature Review.
  Intention prediction is essential for enabling intuitive and adaptive control in upper-limb exoskeletons, especially in dynamic industrial environments. However, the suitability of different cues, sensors, and computational models for real-world industrial app…
  pubmed Hochreiter D, Schmermbeck K, Vazquez-Pufleau M, Ferscha A 2025 Aug 22 置信度 0.82
• Machine Learning Models for Predicting Gynecological Cancers: Advances, Challenges, and Future Directions.
  Gynecological cancer, especially breast, cervical, and ovarian cancer, are significant health issues affecting women worldwide. When screened they are mostly detected at later stages because of non-specific signs and symptoms as well as the unavailability of r…
  pubmed Garg P, Krishna M, Kulkarni P, Horne D 等 2025 Aug 27 置信度 0.82
• The Role of Generative Artificial Intelligence and Large Language Models in Atrial Fibrillation: Clinical Research and Decision Support.
  Atrial fibrillation (AF) is a prevalent and complex cardiac arrhythmia requiring multifaceted management strategies. This review explores the integration of large language models (LLMs) and machine learning into AF care, with a focus on clinical utility, priva…
  pubmed Tran HH, Thu A, Twayana AR, Fuertes A 等 2025 Sep 10 置信度 0.82
• Deep learning methods and applications in single-cell multimodal data integration.
  The integration of multimodal single-cell omics data is a state-of-art strategy for deciphering cellular heterogeneity and gene regulatory mechanisms. Recent advances in single-cell technologies have enabled the comprehensive characterization of cellular state…
  pubmed Nunes FVM, Behrens LMP, Weimer RD, Gonçalves GF 等 2025 Dec 1 置信度 0.82
• Applications of Federated Large Language Model for Adverse Drug Reactions Prediction: Scoping Review.
  Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unst…
  pubmed Guo D, Choo KR 2025 Sep 8 置信度 0.82
• Leveraging artificial intelligence and machine learning in kinase inhibitor development: advances, challenges, and future prospects.
  Protein kinases are central regulators of cell signaling and play pivotal roles in a wide array of diseases, most notably cancer and autoimmune disorders. The clinical success of kinase inhibitors-such as imatinib and osimertinib-has firmly established kinases…
  pubmed Elgawish MS, Almatary AM, Zaitone SA, Salem MSH 2025 Oct 15 置信度 0.82
• Detecting the Undetected: Machine Learning in Early Disease Diagnosis.
  Early detection of diseases is a critical pillar in advancing modern healthcare, offering timely interventions and better patient outcomes. This overview highlights a range of machine learning (ML) approaches that are transforming early disease diagnosis. We d…
  pubmed Rathi K, Sharma S, Barnwal A 2025 Oct 置信度 0.82
• Radiotherapy for primary bone tumors: current techniques and integration of artificial intelligence-a review.
  Primary bone tumours remain among the most challenging indications in radiation oncology-not because of anatomical size or distribution, but because curative intent demands ablative dosing alongside stringent normal-tissue preservation. Over the past decade, t…
  pubmed Tong J, Chen D, Li J, Chen H 等 2025 置信度 0.82
• A robust sampling technique for realistic distribution simulation in federated learning.
  Federated Learning helps training deep learning networks with diverse data from different locations, particularly in restricted clinical settings. However, label distributions overlapping only partially across clients, due to different demographics, may signif…
  pubmed Hoepp R, Rist L, Katzmann A, Ashok R 等 2026 Mar 置信度 0.82
• Computational Architectures for Precision Dairy Nutrition Digital Twins: A Technical Review and Implementation Framework.
  Sensor-enabled digital twins (DTs) are reshaping precision dairy nutrition by seamlessly integrating real-time barn telemetry with advanced biophysical simulations in the cloud. Drawing insights from 122 peer-reviewed studies spanning 2010-2025, this systemati…
  pubmed Rao S, Neethirajan S 2025 Aug 8 置信度 0.82
• Artificial Intelligence-Driven Strategies for Targeted Delivery and Enhanced Stability of RNA-Based Lipid Nanoparticle Cancer Vaccines.
  The convergence of artificial intelligence (AI) and nanomedicine has transformed cancer vaccine development, particularly in optimizing RNA-loaded lipid nanoparticles (LNPs). Stability and targeted delivery are major obstacles to the clinical translation of pr…
  pubmed Bhujel R, Enkmann V, Burgstaller H, Maharjan R 2025 Jul 30 置信度 0.82
• Advanced MRI, Radiomics and Radiogenomics in Unravelling Incidental Glioma Grading and Genetic Status: Where Are We?
  The 2021 WHO classification of brain tumours revolutionised the oncological field by emphasising the role of molecular, genetic and pathogenetic advances in classifying brain tumours. In this context, incidental gliomas have been increasingly identified due to…
  pubmed Guarnera A, Ius T, Romano A, Bagatto D 等 2025 Aug 12 置信度 0.82
• [Artificial intelligence-enhanced ECG interpretation: a new era for electrocardiography?].
  Artificial intelligence (AI) is redefining ECG interpretation, transforming it from a static diagnostic tool into a dynamic, predictive, and integrative instrument. Although widespread, traditional rule-based ECG analysis has limitations in accuracy and adapta…
  pubmed Ricci F, Rizzuto ML, Bisaccia G, Mansour D 等 2025 Sep 置信度 0.82
• Multimodal large language models for medical image diagnosis: Challenges and opportunities.
  The integration of artificial intelligence (AI) into radiology has significantly improved diagnostic accuracy and workflow efficiency. Multimodal large language models (MLLMs), which combine natural language processing (NLP) and computer vision techniques, hol…
  pubmed Zhang A, Zhao E, Wang R, Zhang X 等 2025 Sep 置信度 0.82
• Artificial intelligence in acupuncture: bridging traditional knowledge and precision integrative medicine.
  The integration of artificial intelligence (AI) into acupuncture research is accelerating the transformation of this traditional, experience-based practice into a data-driven, precision discipline. This review synthesizes recent advances in AI-enabled outcome …
  pubmed Hou GL, Dong BQ, Yu BX, Dai JY 等 2025 置信度 0.82
• Multimodal quantitative analysis guides precise preoperative localization of epilepsy.
  Epilepsy surgery efficacy is critically contingent upon the precise localization of the epileptogenic zone (EZ). However, conventional qualitative methods face challenges in achieving accurate localization, integrating multimodal data, and accounting for varia…
  pubmed Shen Y, Shen Z, Huang Y, Wu Z 等 2025 Aug 15 置信度 0.82
• - Invited Review - Advancing precision livestock farming: integrating artificial intelligence and emerging technologies for sustainable livestock management.
  Precision Livestock Farming (PLF) has evolved dramatically from basic monitoring systems to sophisticated artificial intelligence (AI)-driven decision support systems that enhance livestock management efficiency, sustainability, and animal welfare. This review…
  pubmed Tedeschi LO, Guarnido-Lopez P, Menendez Iii HM, Seo S 2026 Apr 置信度 0.82
• Optical Sensor-Based Approaches in Obesity Detection: A Literature Review of Gait Analysis, Pose Estimation, and Human Voxel Modeling.
  Optical sensor technologies are reshaping obesity detection by enabling non-invasive, dynamic analysis of biomechanical and morphological biomarkers. This review synthesizes recent advances in three key areas: optical gait analysis, vision-based pose estimatio…
  pubmed Dhaouadi S, Khelifa MMB, Balti A, Duché P 2025 Jul 25 置信度 0.82
• Machine Learning-Powered Smart Healthcare Systems in the Era of Big Data: Applications, Diagnostic Insights, Challenges, and Ethical Implications.
  Healthcare data rapidly increases, and patients seek customized, effective healthcare services. Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. Unlike previous reviews that separately address AI or big data, th…
  pubmed Rani S, Kumar R, Panda BS, Kumar R 等 2025 Jul 30 置信度 0.82
• Meta-Analysis and Federated Learning over Decentralized Distributed Research Networks.
  Distributed research networks have transformed modern clinical research by enabling large-scale, multi-institutional collaborations while maintaining patient privacy. Two prominent methodologies within these frameworks-meta-analysis and federated learning-addr…
  pubmed Lu Y, Zhang B, Tong J, Chen Y 2025 Aug 置信度 0.82
• Artificial Intelligence in Cardiovascular Imaging: Current Landscape, Clinical Impact, and Future Directions.
  Cardiovascular (CV) imaging is rapidly transforming with the advent of artificial intelligence (AI), automating and augmenting diagnostic pipelines in echocardiography, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear imaging. In this re…
  pubmed Edpuganti S, Shamim A, Gangolli VH, Weerasekara RADKN 等 2025 Apr-Jun 置信度 0.82
• Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions.
  Artificial Intelligence (AI) is emerging as a key driver at the intersection of nutrition and food systems, offering scalable solutions for precision health, smart manufacturing, and sustainable development. This study aims to present a comprehensive review of…
  pubmed Agrawal K, Goktas P, Kumar N, Leung MF 2025 置信度 0.82
• Next-generation smart wound dressings: AI integration, biosensors, and electrospun nanofibers for chronic wound therapy.
  Polymeric biomaterials, particularly electrospun nanofibers, are increasingly central to the development of advanced wound dressings capable of supporting tissue regeneration while enabling real-time physiological monitoring. Chronic wounds associated with dia…
  pubmed Palani N, Mendonce KC, Syed Altaf RR, Mohan A 等 2026 Apr 置信度 0.82
• Artificial intelligence in muscle-invasive bladder cancer: opportunities, challenges, and clinical impact.
  Muscle-invasive bladder cancer (MIBC) represents an aggressive malignancy with significant morbidity and mortality. Recent advances in artificial intelligence (AI) offer promising opportunities to enhance patient care across the entire MIBC management spectrum…
  pubmed Mastroleo F, Marvaso G, Jereczek-Fossa BA 2025 Sep 1 置信度 0.82
• Artificial intelligence in hepatopancreatobiliary surgery for clinical outcome prediction: current perspective and future direction.
  The expanding and evolving role of artificial intelligence (AI) in surgery has been enhanced by the adoption of robotic-assisted surgery (RAS), which provides a platform to facilitate the integration and utilisation of AI technologies. One area where AI is lik…
  pubmed Latif J, Garcea G, Dennison A 2025 Jul 31 置信度 0.82
• Artificial intelligence and machine learning in acute respiratory distress syndrome management: recent advances.
  Acute Respiratory Distress Syndrome (ARDS) remains a critical challenge in intensive care, marked by high mortality and significant patient heterogeneity, which limits the effectiveness of conventional supportive therapies. This review highlights the transform…
  pubmed Li S, Yue R, Lu S, Luo J 等 2025 置信度 0.82
• Artificial Intelligence and ECG: A New Frontier in Cardiac Diagnostics and Prevention.
  Objectives : With the growing importance of mobile technology and artificial intelligence (AI) in healthcare, the development of automated cardiac diagnostic systems has gained strategic significance. This review aims to summarize the current state of knowledg…
  pubmed Bartusik-Aebisher D, Rogóż K, Aebisher D 2025 Jul 9 置信度 0.82
• Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.
  With the rapid advances in artificial intelligence-particularly convolutional neural networks-researchers now exploit CT, PET/CT and other imaging modalities to predict epidermal growth factor receptor (EGFR) mutation status in non-small-cell lung cancer (NSCL…
  pubmed Haixian L, Shu P, Zhao L, Chunfeng L 等 2025 置信度 0.82
• Medical laboratory data-based models: opportunities, obstacles, and solutions.
  Medical Laboratory Data (MLD) models, which combine artificial intelligence with big medical data, have great potential in disease screening, diagnosis, personalized medicine, and health management. This study thoroughly examines the opportunities, challenges,…
  pubmed Meng J, Wu M, Shi F, Xie Y 等 2025 置信度 0.82
• Federated causal discovery with missing data in a multicentric study on endometrial cancer.
  Establishing causal dependencies is crucial in applied domains, such as medicine and healthcare, where decision-making must be explainable. In these settings, small sample sizes and missing data call for federated approaches to maximise the amount of informati…
  pubmed Zanga A, Bernasconi A, Lucas PJF, Pijnenborg H 等 2025 Sep 置信度 0.82
• Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions.
  The integration of artificial intelligence (AI) into next-generation sequencing (NGS) has revolutionized genomics, offering unprecedented advancements in data analysis, accuracy, and scalability. This review explores the synergistic relationship between AI and…
  pubmed Athanasopoulou K, Michalopoulou VI, Scorilas A, Adamopoulos PG 2025 Jun 19 置信度 0.82
• Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review.
  This review synthesizes advancements in artificial intelligence (AI) applications for central serous chorioretinopathy (CSCR), analyzing challenges and outlining future research directions to guide personalized diagnostic and therapeutic strategies.
  pubmed Zhang P, Zhang Q, Hu X, Chi W 等 2025 Sep 置信度 0.82
• Federated Learning-Based Model for Predicting Mortality: Systematic Review and Meta-Analysis.
  The rise of federated learning (FL) as a novel privacy-preserving technology offers the potential to create models collaboratively in a decentralized manner to address confidentiality issues, particularly regarding data privacy. However, there is a scarcity of…
  pubmed Tahir N, Jung CR, Lee SD, Azizah N 等 2025 Jul 21 置信度 0.82
• The Integration of Multi-omics With Artificial Intelligence in Hepatology: A Comprehensive Review of Personalized Medicine, Biomarker Identification, and Drug Discovery.
  The evolution of high-throughput technologies has expanded the role of multi-omics in hepatology, moving away from traditional hypothesis-driven research toward integrative, data-driven models. However, high cost and resource intensity have limited widespread …
  pubmed Ramesh D, Manickavel P, Ghosh S, Bhat M 2025 Nov-Dec 置信度 0.82
• Advances in materials for wearable biosensors.
  Wearable biosensors have emerged as transformative instruments for continuous, non-invasive health monitoring, providing real-time analysis of biomarkers in biofluids such as sweat, interstitial fluid, and saliva. This chapter offers a comprehensive overview o…
  pubmed Maiya D, Patel T, Pandya A 2025 置信度 0.82
• Diabetic retinopathy detection from fundus images: A wide survey from grading to segmentation of lesions.
  Diabetes is one of the most common diseases worldwide and requires accurate diagnosis. Patients with diabetes are often affected by diabetic retinopathy (DR), which can lead to low vision, vision loss, or blindness. Therefore, a robust computer-aided diagnosis…
  pubmed Gautam A, Shanker R 2025 Sep 置信度 0.82
• AI-powered liquid biopsy for early detection of gastrointestinal cancers.
  Gastrointestinal cancers (GICs) are a leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis. Liquid biopsy has emerged as a promising non-invasive diagnostic tool, utilizing circulating tumor DNA (ctDNA), circulating tumor ce…
  pubmed Hussain MS, Rejili M, Khan A, Alshammari SO 等 2025 Sep 1 置信度 0.82
• Human-Centric Cognitive State Recognition Using Physiological Signals: A Systematic Review of Machine Learning Strategies Across Application Domains.
  This systematic review analyses advancements in cognitive state recognition from 2010 to early 2024, evaluating 405 relevant articles from an initial pool of 2398 records identified through five databases: Scopus, Engineering Village, Web of Science, IEEE Xplo…
  pubmed Jin K, Rubio-Solis A, Naik R, Leff D 等 2025 Jul 5 置信度 0.82
• The ethics of data mining in healthcare: challenges, frameworks, and future directions.
  Data mining in healthcare offers transformative insights yet surfaces multilayered ethical and governance challenges that extend beyond privacy alone. Privacy and consent concerns remain paramount when handling sensitive medical data, particularly as healthcar…
  pubmed Ahmed MM, Okesanya OJ, Oweidat M, Othman ZK 等 2025 置信度 0.82
• A scoping review of the governance of federated learning in healthcare.
  In healthcare, federated learning (FL) is emerging as a methodology to enable the analysis of large and disparate datasets while allowing custodians to retain sovereignty. While FL minimises data-sharing challenges, concerns surrounding ethics, privacy, malefi…
  pubmed Eden R, Chukwudi I, Bain C, Barbieri S 等 2025 Jul 10 置信度 0.82
• Systematic review of machine learning applications in the early prediction and management of chronic lymphocytic leukaemia.
  Objective: This review assesses the efficacy of machine learning (ML) models for classification and management of Chronic Lymphocytic Leukaemia (CLL). Methods: Twenty studies published between 2014 and 2023 were reviewed, focusing on supervised ML models to pr…
  pubmed Al-Agil M, Patten PE, Alhaq A 2025 Jul-Sep 置信度 0.82
• Generalizing location-centric variations to enhance contactless human activity recognition.
  Contactless Human Activity Recognition (HAR) has played a critical role in smart healthcare and elderly care homes to monitor patient behavior, detect falls or abnormal activities in real time. The effectiveness of non-invasive HAR is often hindered by locatio…
  pubmed Khan F, Yaseen Shah S, Ahmad J, Al Mazroa A 等 2025 置信度 0.82
• Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.
  Artificial intelligence (AI) is driving a paradigm shift in gastroenterology and hepatology by delivering cutting-edge tools for disease screening, diagnosis, treatment, and prognostic management. Through deep learning, radiomics, and multimodal data integrati…
  pubmed Chen ZL, Wang C, Wang F 2025 Jun 28 置信度 0.82
• Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning.
  Detecting liver tumors via computed tomography (CT) scans is a critical but labor-intensive task. Extensive expert annotations are needed to train effective machine learning models. This study presents an innovative approach that leverages federated learning i…
  pubmed Lee CS, Lien JJ, Chain K, Huang LC 等 2025 Jul 1 置信度 0.82
• Harnessing AI for Improved Diagnosis and Management of Pediatric Sepsis: Current Advances, Challenges, and Future Directions.
  Artificial intelligence (AI) has been applied to early recognition and management of rapidly progressive, community-acquired pediatric sepsis, a leading cause of childhood mortality. The broad adoption of electronic health records combined with rapid advances …
  pubmed Siolos P, Pasha S, Triantafyllou M, Wolff N 等 2025 Jul 1 置信度 0.82
• Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders.
  The complex pathophysiology and diverse manifestations of esophageal disorders pose challenges in clinical practice, particularly in achieving accurate early diagnosis and risk stratification. While traditional approaches rely heavily on subjective interpretat…
  pubmed Liu SW, Li P, Li XQ, Wang Q 等 2025 Jun 21 置信度 0.82