Artificial Intelligence Medical Compendium

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

Showing 371 to 380 of 213,137 articles

Semantic edge detection of fractures in geological outcrops using edge aware dilated convolutional networks.

Scientific reports Jul 21, 2026
Natural fracture networks govern subsurface fluid flow, rock-mass stability, and strain accommodation in the brittle crust, yet their automated delineation from outcrop imagery remains challenging due to multi-scale size variability, low contrast bet... read more 

A Benchmark Dataset for Rat Social and Aggressive Behavior Classification.

Scientific data Jul 21, 2026
Social interactions are central to behavioral and systems neuroscience, yet progress in understanding their neural basis depends on reliable and scalable behavioral quantification. However, standardized datasets, transparent annotation schemes and re... read more 

Generalized graph foundation models as versatile data-driven digital twins for complex technological systems.

Scientific reports Jul 21, 2026
Digital twins are comprised of computational models that mimic the 'as built' characteristics of devices, systems, and networks of systems whose performance in the real world warrants quantitative and critical assessment.The literature on constructin... read more 

The analogy theorem in Hoare logic for formal verification of knowledge transfer in machine learning.

Scientific reports Jul 21, 2026
The introduction of machine learning methods has led to significant advances in automation, optimization, and new discoveries in various fields of science and engineering. However, their widespread application faces a fundamental limitation: the mode... read more 

Personalized content generation in family education based on deep learning.

Scientific reports Jul 21, 2026
The generation of personalized learning content in family environments has been a growing interest in recent years, with the development of new and advanced deep learning techniques. Most previous works use the rule-based system and collaborative fil... read more 

Machine learning-based analysis of risk factors and construction of a predictive model for hyperuricemia in Chinese health examination population.

Scientific reports Jul 21, 2026
Hyperuricemia (HUA) imposes a growing public health burden, calling for better risk stratification tools. In this cross-sectional study of 4906 Chinese adults undergoing routine health checks (overall HUA prevalence: 26.0%), we built machine learning... read more 

Combining residual U-Net and data augmentation for dense temporal segmentation of spike wave discharges in single-channel EEG.

Scientific reports Jul 21, 2026
Manual annotation of spike-wave discharges (SWDs), the electrographic hallmark of absence seizures, is labor-intensive for long-term electroencephalography (EEG) monitoring studies. While machine learning approaches show promise for automated detecti... read more 

Integrating multi-source geoscience data with RF-weighted conditional variational autoencoders (CVAE) for porphyry copper prospectivity mapping: an application to the Ardestan district, Central Iran.

Scientific reports Jul 21, 2026
Porphyry Cu deposits in the Urmia-Dokhtar magmatic belt constitute important exploration targets, yet the heterogeneous and multi-scale nature of geological, geochemical, and remote sensing data introduces substantial uncertainty in prospectivity map... read more 

Dynamic rough set learning for reliable early warning in industrial time-series systems.

Scientific reports Jul 21, 2026
Industrial early-warning systems require models that can detect transitional risk states before failure while also explaining uncertainty in the resulting decisions. Existing machine-learning and deep-learning approaches can achieve strong predictive... read more 

Secure and explainable fraud detection in healthcare claims using blockchain-based machine learning.

Scientific reports Jul 21, 2026
Healthcare insurance fraud causes substantial financial losses, operational inefficiencies, and reduced trust among patients, providers, and insurers. Conventional fraud detection approaches often rely on centralized infrastructures and opaque machin... read more 
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