Artificial Intelligence Medical Compendium

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

Showing 2,151 to 2,160 of 203,255 articles

Slow slip modulates low-frequency seismicity on the Parkfield segment of the San Andreas Fault.

Nature communications Jun 9, 2026
Understanding how slow slip events (SSEs) influence fault behavior is essential for characterizing the fault slip spectrum and its role in earthquake generation. Here, we show that deep learning applied to strainmeter data can detect short-duration S... read more 

A machine-learning-based reconstruction of surface mass balance over the Greenland Ice Sheet from 1950 to 2020.

Scientific data Jun 9, 2026
An accurate estimation of surface mass balance (SMB) is imperative for reliable quantification of the Greenland Ice Sheet (GrIS) mass changes and associated global sea level rise. Here, we present two new reconstructions of gridded annual SMB at the ... read more 

On the generalization limits of quantum generative adversarial networks with pure state generators.

Scientific reports Jun 9, 2026
We investigate the capabilities of quantum generative adversarial networks (QGANs) in image generations tasks. Our analysis centers on fully quantum implementations of both the generator and discriminator. Through extensive numerical testing of curre... read more 

Influence of gravity variations on the activity of neuronal spheroids in an acoustic trap.

NPJ microgravity Jun 9, 2026
Among biological models, cell culture constitutes an important paradigm that allows rapid examination of cell phenotype and behavior. While cell cultures are classically grown on a 2D substrate, the recent development of organoid technologies represe... read more 

Modeling China's deep temperature field with ML-based heat flow and layered heat production data.

Scientific reports Jun 9, 2026
Accurate modeling of the deep crustal temperature field is critical for geothermal resource assessment and understanding lithospheric thermal evolution. Traditional approaches relying on interpolation-based terrestrial heat flow (THF) map and average... read more 

Probabilistic deep learning framework for dynamic carbon emission accounting of electric buses under grid uncertainty.

Scientific reports Jun 9, 2026
The electrification of public transit has emerged as a pivotal pathway for deep urban decarbonization. However, existing carbon accounting methods predominantly rely on static grid emission factors and deterministic energy models, often overlooking s... read more 

AI and knowledge driven computation of rock mass characteristic parameters across engineering projects.

Scientific reports Jun 9, 2026
With the continuous development of underground construction, represented by transportation tunnels, municipal utility tunnels and hydraulic tunnels, accurately perceiving the rock mass quality ahead of the tunnel boring machine (TBM) face has become ... read more 

Hybrid physics-informed machine learning framework for calibration-free degradation prediction of lithium-ion batteries.

Scientific reports Jun 9, 2026
Lithium-ion battery degradation prediction traditionally requires chemistry-specific laboratory calibration, limiting scalability across diverse operating conditions and cathode materials. This work proposes a Hybrid Physics-Informed Machine Learning... read more 

Performance enhancement of THz antenna with dielectric resonator and prediction bandwidth using machine learning approach for 6G applications.

Scientific reports Jun 9, 2026
The need for ultra-low latency and ultra-wideband in 6G applications requires efficient solutions for dielectric resonator antenna design. This paper presents the results of using a machine learning approach to improve the performance of a dielectric... read more 

Adaptive graph-evolutionary framework for dynamic feature refinement in multi-label learning.

Scientific reports Jun 9, 2026
Multi-label learning in high-dimensional environments is fundamentally challenged by complex interdependencies among features and labels, as well as redundancy and noise that degrade predictive performance and scalability. Conventional feature select... read more 
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