Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 58,601 to 58,610 of 227,634 articles

A wearable monitoring system for running gait analysis by diffusion transformer.

PloS one Jan 23, 2026
Conventional wearable monitoring devices often suffer from insufficient data accuracy and low posture recognition rates, making them inadequate for the demands of professional sports health monitoring. To address these issues, this study proposes a w... read more 

Machinability and tribological optimization of origami-inspired Almond Shell-PMMA via RSM, ML, and TOPSIS.

PloS one Jan 23, 2026
An integrated approach combining Response Surface Methodology (RSM), Machine Learning (ML-SVM) and TOPSIS optimization method is applied in this study to analyse the tribological behaviour of 3D printed patterns of almond shell-PMMA (polymethyl metha... read more 

Effectiveness of roadside alcohol testing in reducing fatal accidents and fatal drinking-driving accidents: A multi-city study in China.

PloS one Jan 23, 2026
This study examines the dynamic relationship between roadside alcohol check rates and traffic mortality across 248 cities in mainland China from 2014 to 2020. Using a dataset comprising 365,753 roadside check arrests, 227,896 traffic deaths, and 21,0... read more 

DualSightNet: A novel dual architecture for visual quality control of railway infrastructure.

PloS one Jan 23, 2026
To ensure the operational safety of trains, it is essential to monitor the condition of the rails. In order to detect problems ranging from wear and tear to possible sabotage, no comprehensive and continuous monitoring is carried out today. The objec... read more 

Optical photothermal infrared (OPTIR) spectroscopy assisted by machine learning for lactic acid bacteria identification at strain level.

The Analyst Jan 23, 2026
Lactic acid bacteria (LAB) are widely used in food, health, and biotechnology sectors, where accurate strain level identification is critical. Conventional methods, such as 16S rRNA sequencing, PCR-based fingerprinting (RAPD, AFLP), and MALDI-TOF mas... read more 

LeafAI: Interpretable plant disease detection for edge computing.

PloS one Jan 23, 2026
In real-world agriculture, healthy plant leaves are significantly more common than diseased ones. This natural class imbalance presents challenges in automated plant disease detection, as analyzing each leaf with computationally intensive deep-learni... read more 

Protein language models trained on biophysical dynamics inform mutation effects.

Proceedings of the National Academy of Sciences of the United States of America Jan 23, 2026
Structural dynamics are fundamental to protein functions and mutation effects. Current protein deep learning models are predominantly trained on sequence and/or static structure data, which often fail to capture the dynamic nature of proteins. To add... read more 

Effect of sea surface temperature in El Niño regions on dengue dynamics in Colombia: Evidence from causal machine learning.

PLOS global public health Jan 23, 2026
Dengue fever is among the most rapidly expanding vector-borne diseases globally, with Colombia ranking among the most affected countries in the Americas. Although previous research has linked climate variability and El Niño-Southern Oscillation (ENSO... read more 

DVG-Diffusion: Dual-View Guided Diffusion Model for CT Reconstruction from X-Rays.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Jan 23, 2026
Directly reconstructing 3D CT volume from few-view 2D X-rays using an end-to-end deep learning network is a challenging task, as X-ray images are merely projection views of the 3D CT volume. In this work, we facilitate complex 2D X-ray image to 3D CT... read more 

U-RWKV: Accurate and Efficient Volumetric Medical Image Segmentation via RWKV.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Jan 23, 2026
Accurate and efficient volumetric medical image segmentation is vital for clinical diagnosis, pre-operative planning, and disease-progression monitoring. Conventional convolutional neural networks (CNNs) struggle to capture long-range contextual info... read more 
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