1.681 hasil
Menampilkan 1–20 · 0,31 detik · RSS
Informatics in Medicine Unlocked
Scopus
ISSN 2352-9148 · Elsevier · 1.681 artikel di indeks
-
Artikel Jurnal
Cross-procedure generalisation of an AI algorithm for surgical step prediction: from distal radius to fibula osteosynthesis
Artificial intelligence (AI) methods for surgical step recognition have shown promising results, but their ability to generalise across different types of surgery remains a critical challenge. This study aimed to assess whether an AI algorithm designed for step prediction in videos of plate…
-
Artikel Jurnal
When classical encryption fails: A non-invasive post-quantum security layer for medical image transfers
Quantum computing threatens the long-term security of widely deployed public-key cryptography used to protect medical image transfer. The Digital Imaging and Communications in Medicine (DICOM) standard is the dominant format and protocol suite for medical imaging workflows and is commonly…
-
Artikel Jurnal
Diabetes risk modeling through tabular-to-image transformations and ensemble learning
Diabetes is a rapidly growing global health concern, and early detection and personalized risk assessment are critical for preventing severe complications. However, current predictive approaches often struggle with robustness, scalability, and class imbalance in clinical datasets. To address these…
-
Artikel Jurnal
Development of a neural network system for predicting in-hospital pressure ulcers in spinal trauma patients
Background and objectives: This study aimed to developing and to deploying an optimal machine learning model to predict pressure ulcers (PUs) in hospitalized patients with spinal fractures, using data from the National Spinal Cord and Column Injury Registry of Iran (NSCIR-IR). Methods: Data were…
-
Artikel Jurnal
Time-critical quality assessment of a digital hub for acute stroke care: Implementing key performance indicators within the CAEHR (CArdiovascular diseases – Enhancing Healthcare through cross-sectoral Routine data integration) project
Purpose: Timely treatment for acute stroke requires rapid admission to specialized centers, which is challenging in rural areas. Hereby we report: the conceptualization of implementing digital interfaces to improve time-critical communication between Emergency Medical Services (EMS), regional…
-
Artikel Jurnal
Systematic review of transformers, graph neural networks, and federated learning in oncology: Applications, challenges, and pathways to clinical translation
Background: Advanced deep learning models, including transformers, graph neural networks (GNNs), and federated learning (FL), are reshaping oncology by enhancing the analysis of complex, multimodal cancer data for diagnostic and prognostic purposes. Despite their promise, challenges in…
-
Artikel Jurnal
Fundus imaging based quantum computing: Grading of severity of vision threatening diabetic retinopathy
Diabetic Retinopathy (DR) is a major complication of diabetes that can result in irreversible vision loss if not detected early. Manual ophthalmologic screening is time-consuming and subject to subjectivity, underscoring the need for accurate and automated diagnostic systems. This study proposes…
-
Artikel Jurnal
Overcoming technical barriers in healthcare with blockchain: A systematic review
The rising digitalization of healthcare has increased reliance on complex information systems, creating the need for better integration and interoperability. Despite technology developments, healthcare organizations still confront technical challenges that restrict data transmission, scalability…
-
Artikel Jurnal
Who's afraid of synthetic data? Hybrid approaches to deliver medical digital twins
Despite rapidly growing volumes of clinical data, precision medicine still faces a structural data deficit: most patients and rare disease variants are sparsely sampled, labels are noisy, and counterfactual outcomes for alternative treatments are fundamentally unobservable. This position paper…
-
Artikel Jurnal
Clinical validation of artificial intelligence for gastrointestinal diseases
Artificial intelligence (AI), particularly deep convolutional neural networks (DCNNs), has demonstrated significant potential for transforming the diagnosis and management of gastrointestinal (GI) diseases. This review critically examines the evolving landscape of AI applications in…
-
Artikel Jurnal
AutoLungDx: A hybrid deep learning approach for early lung cancer diagnosis using 3D Res-U-Net, YOLOv5, and vision transformers
Objective:: Lung cancer is a leading cause of cancer-related death worldwide, and early detection is crucial to improving patient outcomes. However, early diagnosis is a major challenge, particularly in low-resource settings with limited access to CT resources and trained radiologists. This study…
-
Artikel Jurnal
Advancement and challenges of reinforcement learning in lung cancer imaging
Understanding how reinforcement learning can be applied to lung cancer imaging is essential for progress in this field. Despite increasing interest, there is a clear lack of focused survey papers that explore this intersection. To fill this gap, we conducted a comprehensive review that brings…
-
Artikel Jurnal
Integrating knowledge graph embeddings with clinical data: A case study on Acute Kidney Injury prediction
The integration of artificial intelligence (AI) and machine learning (ML) in healthcare has significantly advanced predictive analytics. Acute Kidney Injury (AKI), a condition marked by sudden kidney function loss, necessitates early intervention to improve patient outcomes. This study introduces a…
-
Artikel Jurnal
Interpretable deep learning for rotator cuff tear diagnosis: A novel convolutional neural network with Grad-CAM visualization on MRI
Accurate diagnosis of rotator cuff tears from magnetic resonance imaging (MRI) is essential for effective clinical management and treatment planning. In this study, we propose a novel convolutional neural network (CNN) architecture specifically designed for classifying rotator cuff tears…
-
Artikel Jurnal
Epic overhaul at a Canadian hospital: Pre-Post evaluation insights from physicians and medical residents
Background: The implementation of Electronic Medical Records (EMRs) in hospitals offers potential benefits but often disrupts clinician workflows, affecting care delivery and outcomes. Objective: This study evaluates physicians' and medical residents’ perspectives on the impacts of introducing a…
-
Artikel Jurnal
Impact assessment of digital ecosystem in healthcare services: A qualitative case study of hospital data management in Bikaner District in India
The proliferation of digitalization, along with advanced computational techniques, in the healthcare ecosystem has expedited the process of patient care, treatment, and disease diagnosis globally. Medical research, especially involving computational techniques, is heavily dependent on the…
-
Artikel Jurnal
Prediction versus inference: A dual-pipeline analytical framework for evaluating GAIN-based imputation in hypertension risk modeling
Generative Adversarial Imputation Networks (GAIN) are increasingly used to address missing data and improve clinical prediction models, but whether GAIN-imputed data preserve reliable regression-based inference remains unclear. This study compared two analytical strategies for hypertension risk…
-
Artikel Jurnal
TIDE: A methodological framework for extracting and validating clinical trajectories from non-temporal administrative data
Objective:: Longitudinal trajectories are vital for clinical predictive modeling, yet administrative healthcare datasets exhibit low temporal resolution. This obscures chronological sequence extraction, making traditional timestamp-dependent algorithms ineffective. We propose the TIDE (Topological…
-
Artikel Jurnal
CXRMate-2: Structured multimodal temporal embeddings and tractable reinforcement learning for clinically acceptable chest X-ray radiology report generation
Chest X-ray (CXR) radiology report generation (RRG) models have made rapid progress on automated metrics, yet their clinical utility remains uncertain because few have undergone qualitative evaluation by radiologists. We present CXRMate-2, a state-of-the-art CXR RRG model designed to make…
-
Artikel Jurnal
Explainable early sepsis prediction using hybrid TCN-ransformer and retrieval-augmented clinical reasoning
Sepsis is a life-threatening infection associated with a dysregulated host response, which is one of the major causes of death in intensive care units around the world. Early detection of the onset of sepsis is essential to enhancing patient outcomes, however the rule-based scoring systems can only…
Menampilkan maksimal 50 halaman. Persempit pencarian dengan filter untuk hasil yang lebih spesifik.