Artificial Intelligence Medical Compendium
Explore the latest research on artificial intelligence and machine learning in medicine.
Showing 16,361 to 16,370 of 213,568 articles
Single-cell approach dissecting agr quorum sensing dynamics in Staphylococcus aureus.
Nature communications
May 20, 2026
Quorum sensing (QS) enables bacteria to coordinate collective behaviors by secreting and sensing diffusible signals. Understanding QS at single-cell resolution is essential because population-level measurements often obscure regulatory heterogeneity.... read more
Systematic review and meta-analysis of machine learning models predicting massive hemorrhage protocol in trauma.
Scientific reports
May 20, 2026
Timely activation of massive hemorrhage protocols (MHP) is critical to prevent exsanguination and improve survival in trauma patients. Current clinical tools for predicting MHP need have limited sensitivity and moderate specificity. Machine learning ... read more
Enhancing esophageal cancer detection using a deep learning framework and a novel spectrum-aided vision enhancer for virtual narrow band imaging.
Scientific reports
May 20, 2026
Esophageal cancer is a highly aggressive malignancy where early detection is critical for survival. However, early-stage lesions typically present subtle mucosal changes that are difficult to identify using standard White Light Imaging (WLI), and har... read more
Deep learning-based neonatal outcome prediction: an LSTM autoencoder framework for pattern analysis and risk assessment.
Scientific reports
May 20, 2026
The phenomenon of neonatal mortality is a significant issue in health care, which necessitates the use of advanced analytical tools for early risk prediction. This work utilized the Medical Information Mart (MIMIC) Pediatric Intensive Care (PIC) data... read more
External validation of cough-based algorithms for pulmonary tuberculosis screening from the CODA TB DREAM challenge using cough data from Peru.
Scientific reports
May 20, 2026
The COugh Diagnostic Algorithm for Tuberculosis (CODA TB) DREAM Challenge recently evaluated the performance of artificial intelligence (AI) algorithms for tuberculosis (TB) screening using cough sounds. Eleven AI models were developed using a datase... read more
Evaluation of YOLOv7-v13 models for multi-class small insect pest detection using the five-pest dataset.
Scientific reports
May 20, 2026
Pest infestation affects global agriculture, causing massive crop yield losses, ecosystem degradation, and negative economic impacts. High accuracy and efficiency in detecting small insect pests are crucial to avoid pesticide overuse and biodiversity... read more
A neural network embedding the linear-quadratic model for improved prediction of cellular response to ion beam exposure.
Scientific reports
May 20, 2026
Predicting biological responses to ionizing radiation is challenging due to the complex, multi-scale mechanisms involved. Traditional machine learning (ML) models that fit experimental data on cell survival can be sensitive to experimental uncertaint... read more
AI-driven insights into the impact of tourism on local cultures: a machine learning approach.
Scientific reports
May 20, 2026
Tourism is increasingly recognized as a powerful driver of economic growth, spatial restructuring, and cultural change, yet empirical evidence on how tourism intensity and short-term rentals reshape local cultures remains fragmented. Existing researc... read more
Hybrid machine learning for SCC strength prediction using metaheuristic optimization.
Scientific reports
May 20, 2026
Self-Compacting Concrete (SCC) represents a significant innovation in modern concrete technology owing to its excellent flowability, workability, and mechanical performance. Accurate prediction of SCC compressive strength is essential for optimizing ... read more
SHAP analysis of an improved EEG-based mental workload classification framework: utilizing data augmentation and explainable AI.
Scientific reports
May 20, 2026
Mental workload (MWL) classification using electroencephalogram (EEG) signals is crucial for cognitive neuroscience and is also a challenging research area in brain-computer interface (BCI). Since the EEG signals fluctuate a lot across sessions and i... read more
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