Artificial Intelligence Medical Compendium

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

Showing 60,501 to 60,510 of 228,072 articles

A comprehensive biomechanical phenotyping framework for diabetic foot ulcer risk stratification using multi-modal gait analysis and machine learning.

Clinical biomechanics (Bristol, Avon) Jan 17, 2026
BACKGROUND: Current diabetic foot ulcer risk assessment methods lack precision in identifying high-risk biomechanical phenotypes. This study aimed to develop a comprehensive biomechanical profiling framework integrating multi-modal gait analysis with... read more 

FRM-PTQ: Feature relationship matching enhanced low-bit post-training quantization for large language models.

Neural networks : the official journal of the International Neural Network Society Jan 17, 2026
Post-Training Quantization (PTQ) has emerged as an effective approach to reduce memory and computational demands during LLMs inference. However, existing PTQ methods are highly sensitive to ultra-low-bit quantization with significant performance loss... read more 

Point-of-need and portable sensing strategies for quality and safety assessment of Traditional Chinese Medicine products: A critical analytical review.

Analytica chimica acta Jan 17, 2026
Sensor technology has emerged as a transformative tool for point-of-need and portable quality control and safety assessment of Traditional Chinese Medicine (TCM) products. Although separation and detection technologies have improved, there is still a... read more 

Integrating GC-MS, high-throughput sequencing, and machine learning to elucidate flavor development and microbial succession in Italian-style dry-cured ham.

Food research international (Ottawa, Ont.) Jan 17, 2026
This study investigated effects of ripening on physicochemical, aroma profiles, and microbial succession in Italian-style dry-cured ham (ISDCH). Results showed that moisture content and pH decreased significantly during ripening, while the proteolyti... read more 

Intelligent recognition of the fermentation stage of baijiu based on multi-dimensional data fusion and interpretable machine learning.

Food research international (Ottawa, Ont.) Jan 17, 2026
In the traditional solid-state fermentation of Baijiu, production control primarily relies on human expertise and retrospective offline analysis due to the lack of real-time, objective assessment methods. To address this limitation, this study introd... read more 

Adversarial Drift-Aware Predictive Transfer: Toward Durable Clinical AI

arXiv Jan 17, 2026
Clinical AI systems frequently suffer performance decay post-deployment due to temporal data shifts, such as evolving populations, diagnostic coding updates (e.g., ICD-9 to ICD-10), and systemic shocks like the COVID-19 pandemic. Addressing this ``ag... read more 
Applications cs.LG stat.ME

Accelerated MR Elastography Using Learned Neural Network Representation

arXiv Jan 17, 2026
To develop a deep-learning method for achieving fast high-resolution MR elastography from highly undersampled data without the need of high-quality training dataset. We first framed the deep neural network representation as a nonlinear extension of t... read more 
Signal Processing cs.CV cs.LG q-bio.QM

Digital FAST: An AI-Driven Multimodal Framework for Rapid and Early Stroke Screening

arXiv Jan 17, 2026
Early identification of stroke symptoms is essential for enabling timely intervention and improving patient outcomes, particularly in prehospital settings. This study presents a fast, non-invasive multimodal deep learning framework for automatic bina... read more 
Computer Vision and Pattern Recognition

Task-tailored Pre-processing: Fair Downstream Supervised Learning

arXiv Jan 17, 2026
Fairness-aware machine learning has recently attracted various communities to mitigate discrimination against certain societal groups in data-driven tasks. For fair supervised learning, particularly in pre-processing, there have been two main categor... read more 
Machine Learning stat.ME stat.ML

RemoteVAR: Autoregressive Visual Modeling for Remote Sensing Change Detection

arXiv Jan 17, 2026
Remote sensing change detection aims to localize and characterize scene changes between two time points and is central to applications such as environmental monitoring and disaster assessment. Meanwhile, visual autoregressive models (VARs) have recen... read more 
Computer Vision and Pattern Recognition
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