Artificial Intelligence Medical Compendium

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

Showing 14,621 to 14,630 of 212,071 articles

Challenges of Ontology-Based Concept Normalization for Deep Phenotyping in Rare Skin Diseases.

Studies in health technology and informatics May 21, 2026
Extracting and standardizing phenotypic information from free-text medical reports remains a main challenge in biomedical natural language processing (NLP). Conceptual normalization, which maps textual mentions to standardized vocabularies such as th... read more 

Towards Real-Time Decision Support with LIVIA: Live-Streaming Infrastructure for Vital Sign Inference and Analytics.

Studies in health technology and informatics May 21, 2026
The translation of machine learning (ML) research into clinical practice is slowed by fragmented infrastructure and lack of standardized pipelines. LIVIA provides a platform for real-time ML in intensive care, integrating high-frequency physiological... read more 

Generating synthetic CT from MR images for radiation-free computer-assisted quantification of hip morphology.

European journal of radiology May 21, 2026
OBJECTIVES: To compare MR image-based synthetic CT (sCT) with conventional CT for computer-assisted quantification of hip morphology by evaluating osseous structure segmentation quality and hip morphological parameters. MATERIALS AND METHODS: This re... read more 

Predicting PROTAC degradation activity and selectivity of effective E3 ligase through harnessing a combination of AtomPair fingerprints and multiple machine learning algorithms.

Journal of molecular graphics & modelling May 21, 2026
Proteolysis targeting chimeras (PROTACs) are drugs that recruit target proteins and E3 ligases to leverage the ubiquitin-proteasome system (UPS) for specific degradation of proteins. This approach has received significant attention because of their a... read more 

Decoding the role of polyamine metabolism in lung adenocarcinoma prognosis: A triangulated approach combining transcriptome, single-cell and Mendelian randomization analyses.

Oncology letters May 21, 2026
The progression of lung adenocarcinoma (LUAD) is influenced by polyamine metabolism, which modulates antitumor immunity, although the underlying mechanisms remain unclear. The present study investigates the role of polyamine metabolism-related genes ... read more 

Bernini: Latent Semantic Planning for Video Diffusion

arXiv May 21, 2026
Multimodal large language models (MLLMs) and diffusion models have each reached remarkable maturity: MLLMs excel at reasoning over heterogeneous multimodal inputs with strong semantic grounding, while diffusion models synthesize images and videos wit... read more 
Computer Vision and Pattern Recognition cs.AI cs.MM

QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks

arXiv May 21, 2026
Low-bit quantization is widely used to compress super-resolution (SR) models and reduce storage and computation costs for deployment on resource-limited devices. However, when SR models are pushed to ultra-low precision (2-4 bits), performance can dr... read more 
Computer Vision and Pattern Recognition

VEELA: A Clinically-Constrained Benchmark for Liver Vessel Segmentation in Computed Tomography Angiography

arXiv May 21, 2026
Accurate segmentation of hepatic and portal vessels in contrast-enhanced computed tomography angiography (CTA) remains challenging due to complex vascular topology, peripheral visibility limitations, and acquisition-induced ambiguities. While existin... read more 
Computer Vision and Pattern Recognition cs.AI

AgroTools: A Benchmark for Tool-Augmented Multimodal Agents in Agriculture

arXiv May 21, 2026
Agricultural decision-making increasingly requires multimodal systems that can transform visual observations into reliable, executable actions. However, existing agricultural multimodal benchmarks mainly evaluate final-answer correctness and provide ... read more 
Computer Vision and Pattern Recognition

ASAP: Attention Sink Anchored Pruning

arXiv May 21, 2026
Vision Transformers (ViTs) face severe computational bottlenecks due to the quadratic complexity of self-attention at high resolutions. Existing token reduction methods rely on local metrics - such as single-layer attention scores - that are inherent... read more 
Machine Learning
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