Artificial Intelligence Medical Compendium

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

Showing 39,721 to 39,730 of 223,737 articles

Explainable machine learning reveals how molecular descriptors govern micropollutant degradation in UV/H2O2 oxidation.

Water research Mar 22, 2026
Micropollutants (MPs) pose significant risks to aquatic ecosystems and human health because of their persistence and potential for bioaccumulation. UV/H2O2 oxidation effectively degrades a wide range of MPs through the generation of hydroxyl radicals... read more 

Quantification via gaussian latent space representations.

Neural networks : the official journal of the International Neural Network Society Mar 22, 2026
Quantification, or prevalence estimation, is the task of predicting the prevalence of each class within an unknown bag of examples. Most existing quantification methods in the literature rely on prior probability shift assumptions to create a quantif... read more 

Enhancing cross-regional transferability of super-resolution-based flood surrogate models for data-scarce catchments.

Water research Mar 22, 2026
Deep learning-based flood surrogate models have shown promise in accelerating spatiotemporal flood simulations, yet their cross-regional transferability remains a significant challenge, limiting widespread application in data-scarce catchments. This ... read more 

Fractional-order gradient descent learning for Elman neural networks.

Neural networks : the official journal of the International Neural Network Society Mar 22, 2026
To address the limitations of conventional integer-order gradient descent in training Elman neural networks, such as susceptibility to local minima and slow convergence-this paper proposes a fractional-order gradient descent learning algorithm for El... read more 

Federated learning with noisy labels: A comprehensive and concise review of current methodologies and future directions.

Neural networks : the official journal of the International Neural Network Society Mar 22, 2026
Federated learning, a vital paradigm in modern machine learning, enables private and decentralised training of models that is crucial for learning from sensitive data. Noisy label learning, another vital paradigm in modern machine learning, addresses... read more 

Consistent but Dangerous: Per-Sample Safety Classification Reveals False Reliability in Medical Vision-Language Models

arXiv Mar 22, 2026
Consistency under paraphrase, the property that semantically equivalent prompts yield identical predictions, is increasingly used as a proxy for reliability when deploying medical vision-language models (VLMs). We show this proxy is fundamentally fla... read more 
Computer Vision and Pattern Recognition

SkinCLIP-VL: Consistency-Aware Vision-Language Learning for Multimodal Skin Cancer Diagnosis

arXiv Mar 22, 2026
The deployment of vision-language models (VLMs) in dermatology is hindered by the trilemma of high computational costs, extreme data scarcity, and the black-box nature of deep learning. To address these challenges, we present SkinCLIP-VL, a resource-... read more 
Computer Vision and Pattern Recognition

Characterizing Long-Range Dependencies in Knee Joint Contact Mechanics: A Comparison of Topology Diffusion, Global Routing, and Hybrid Graph Neural Networks

arXiv Mar 22, 2026
Finite element analysis of knee joint contact mechanics is computationally expensive, which has motivated the development of graph neural network surrogate models. However, effectively representing long-range dependencies in joint mechanical response... read more 
Quantitative Methods

Statistical Learning for Latent Embedding Alignment with Application to Brain Encoding and Decoding

arXiv Mar 22, 2026
Brain encoding and decoding aims to understand the relationship between external stimuli and brain activities, and is a fundamental problem in neuroscience. In this article, we study latent embedding alignment for brain encoding and decoding, with a ... read more 
Methodology cs.LG

LPNSR: Prior-Enhanced Diffusion Image Super-Resolution via LR-Guided Noise Prediction

arXiv Mar 22, 2026
Diffusion-based image super-resolution (SR), which aims to reconstruct high-resolution (HR) images from corresponding low-resolution (LR) observations, faces a fundamental trade-off between inference efficiency and reconstruction quality. The state-o... read more 
Computer Vision and Pattern Recognition cs.AI
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