Artificial Intelligence Medical Compendium

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

Showing 36,831 to 36,840 of 223,469 articles

Reinforcement Learning-Based Sequential Parameter Tuning for Image Signal Processing.

IEEE transactions on pattern analysis and machine intelligence Apr 1, 2026
Hardware image signal processing (ISP) transforms RAW inputs into high-quality RGB images through a series of processing modules, each with numerous tunable parameters. Traditionally, these parameters are manually tuned by imaging experts, a time-con... read more 

A Unified Experience Replay Framework for Spiking Deep Reinforcement Learning.

IEEE transactions on pattern analysis and machine intelligence Apr 1, 2026
Deep Reinforcement Learning (DRL) methods have shown remarkable success in many applications, yet their high energy consumption limits their practicability. Recent studies incorporated energy-efficient Spiking Neural Networks (SNNs) to build Spiking ... read more 

Unveiling Fine-Grained Deceptive Patterns in Multimodal Fake News: An Explainable Neuro-Symbolic Framework With LVLMs.

IEEE transactions on pattern analysis and machine intelligence Apr 1, 2026
The widespread proliferation of fake news on the Internet, especially in multi-modal formats, poses a substantial threat to society. Most deep learning-based approaches for fake news detection yield accurate predictions but lack explainability. Exist... read more 

Developing Evolving Adaptability in Biological Intelligence: A Novel Biologically-Inspired Continual Learning Model for Video Saliency Prediction.

IEEE transactions on pattern analysis and machine intelligence Apr 1, 2026
In the era of deep learning, video saliency prediction task still remains major challenge due to the issue of catastrophic forgetting during feature learning. Most prior works commonly employ generative replay strategies to generate pseudo-samples fr... read more 

CompleMatch: Boosting Time-Series Semi-Supervised Classification With Temporal-Frequency Complementarity.

IEEE transactions on pattern analysis and machine intelligence Apr 1, 2026
Time series Semi-Supervised Classification (SSC) aims to improve model performance by utilizing abundant unlabeled data in scenarios where labeled samples are limited. Previous approaches mainly focus on exploiting temporal dependencies within the ti... read more 

Augmenting Iterative Trajectory for Bilevel Optimization: Methodology, Analysis and Extensions.

IEEE transactions on pattern analysis and machine intelligence Apr 1, 2026
In recent years, there has been a surge of machine learning applications developed with hierarchical structure, which can be approached from Bi-Level Optimization (BLO) perspective. However, most existing gradient-based methods overlook the interdepe... read more 

Predictive Value of Machine Learning for Mortality Risk in Acute Pancreatitis: A Systematic Review and Meta-Analysis.

Journal of clinical gastroenterology Apr 1, 2026
BACKGROUND: Acute pancreatitis (AP), a common acute abdominal disease, has a high mortality rate in severe cases. Accurate mortality prediction is crucial for clinical decision-making. Machine learning (ML) models have shown potential in predicting A... read more 

Hot flashes linked to linguistic markers of cognitive impairment: observational study of social media posts.

Menopause (New York, N.Y.) Apr 1, 2026
OBJECTIVES: This study analyzes social media data from Reddit, using artificial intelligence and natural language processing, to explore cognitive changes in the menopause transition and their associations with hot flashes and hormone therapy (HT). M... read more 

Risk Prediction Models for Post-Endoscopic Retrograde Cholangiopancreatography Pancreatitis: A Systematic Review and Meta-Analysis.

Pancreas Apr 1, 2026
BACKGROUND: Endoscopic retrograde cholangiopancreatography (ERCP) serves as an essential procedure for diagnosing and treating pancreaticobiliary disorders, however it frequently results in post-ERCP pancreatitis (PEP), its most common complication. ... read more 

A Novel Approach to GNN Explainability: Distilling Knowledge With Inter-Layer Alignment.

IEEE transactions on pattern analysis and machine intelligence Apr 1, 2026
Graph Neural Networks (GNNs) have made significant strides in the analysis and modeling of complex network data, particularly excelling in graph and node classification tasks. However, the "closed box" nature of GNNs impedes user understanding and tr... read more 
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