Artificial Intelligence Medical Compendium

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

Showing 51,481 to 51,490 of 225,182 articles

Multi-modal AI for opportunistic screening, staging and progression risk stratification of steatotic liver disease.

Nature communications Feb 11, 2026
The global rise in steatotic liver disease poses a significant public health challenge. While non-contrast computed tomography scans hold promise for opportunistic detection of steatotic liver disease, their potential for staging and risk assessment ... read more 

Biomedical Data Manifest: A lightweight data documentation mapping to increase transparency for AI/ML.

Scientific data Feb 11, 2026
Biomedical machine learning (ML) models raise critical concerns about embedded assumptions influencing clinical decision-making, necessitating robust documentation frameworks for datasets that are shared via external repositories. Fairness-aware algo... read more 

The human metabolome and machine learning improves predictions of the post-mortem interval.

Nature communications Feb 11, 2026
An accurate prediction of the time since death, known as the post-mortem interval, remains a critical research question in forensic and police investigations. Current methods, such as rectal temperature and vitreous potassium levels, only provide rel... read more 

Terrestrial and Airborne Laser Scanning Dataset of Trees in the Shivalik Range, India with Field Measurements and Leaf-Wood Classifications.

Scientific data Feb 11, 2026
Annotated datasets are essential for training and evaluating machine learning models in forest ecology. This dataset provides high-resolution, annotated LiDAR point clouds of 674 individual trees from 12 forest plots in the Shivalik Range of northern... read more 

Seismocardiography Pig Hypovolemia Dataset for Signal Quality Indexing and Validated Cardiac Timings.

Scientific data Feb 11, 2026
Seismocardiography (SCG), a non-invasive method for capturing cardio-mechanical signals, is often susceptible to noise and motion artifacts. Current approaches primarily use automated algorithms and machine learning techniques for signal quality inde... read more 

A two-stage deep learning framework for kidney disease detection using modified specular-free imaging and EfficientNetB2.

Scientific reports Feb 11, 2026
Kidney diseases represent a substantial public health concern, with their incidence increasing markedly over the past decade. Addressing this challenge, our research introduces a sophisticated two-stage diagnostic model for enhancing the detection ac... read more 

Polysomnography Dataset for Sleep Analysis in Ischemic Stroke Patients.

Scientific data Feb 11, 2026
Sleep architecture and integrity significantly influence neural recovery and cognitive restoration. These are particularly relevant in ischemic stroke survivors where sleep-disordered breathing (SDB) is a common comorbidity. To address the lack of st... read more 

A physics-informed graph neural network to approximate docking-based binding affinity for DYRK2 in Alzheimer's drug repurposing.

Scientific reports Feb 11, 2026
Alzheimer's disease (AD) requires the discovery of new therapeutic targets, but traditional molecular docking methods for virtual screening are often computationally expensive. This study introduces PhysDual-GCN, a physics-informed graph neural netwo... read more 

Multi-step chestnut physical characteristics classification model based on vision transformation using a single-view RGB image.

Scientific reports Feb 11, 2026
Chestnut classification is essential for improving postharvest processing efficiency and supporting large-scale commercialization; however, conventional manual sorting is labor intensive, inconsistent, and unsuitable for high-throughput operations. T... read more 

A VLM guided network coupling degradation modeling for degradation aware infrared and visible image fusion.

Scientific reports Feb 11, 2026
Existing Infrared and Visible Image Fusion (IVIF) methods typically assume high-quality inputs. However, when handing degraded images, these methods heavily rely on manually switching between different pre-processing techniques. This decoupling of de... read more 
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