Artificial Intelligence Medical Compendium

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

Showing 38,901 to 38,910 of 223,469 articles

Semantic Iterative Reconstruction: One-Shot Universal Anomaly Detection

arXiv Mar 24, 2026
Unsupervised medical anomaly detection is severely limited by the scarcity of normal training samples. Existing methods typically train dedicated models for each dataset or disease, requiring hundreds of normal images per task and lacking cross-modal... read more 
Computer Vision and Pattern Recognition

Learning Cross-Joint Attention for Generalizable Video-Based Seizure Detection

arXiv Mar 24, 2026
Automated seizure detection from long-term clinical videos can substantially reduce manual review time and enable real-time monitoring. However, existing video-based methods often struggle to generalize to unseen subjects due to background bias and r... read more 
Computer Vision and Pattern Recognition

Detection and Classification of (Pre)Cancerous Cells in Pap Smears: An Ensemble Strategy for the RIVA Cervical Cytology Challenge

arXiv Mar 24, 2026
Automated detection and classification of cervical cells in conventional Pap smear images can strengthen cervical cancer screening at scale by reducing manual workload, improving triage, and increasing consistency across readers. However, it is chall... read more 
Computer Vision and Pattern Recognition

Bi-CRCL: Bidirectional Conservative-Radical Complementary Learning with Pre-trained Foundation Models for Class-incremental Medical Image Analysis

arXiv Mar 24, 2026
Class-incremental learning (CIL) in medical image-guided diagnosis requires retaining prior diagnostic knowledge while adapting to newly emerging disease categories, which is critical for scalable clinical deployment. This problem is particularly cha... read more 
Computer Vision and Pattern Recognition

CDMT-EHR: A Continuous-Time Diffusion Framework for Generating Mixed-Type Time-Series Electronic Health Records

arXiv Mar 24, 2026
Electronic health records (EHRs) are invaluable for clinical research, yet privacy concerns severely restrict data sharing. Synthetic data generation offers a promising solution, but EHRs present unique challenges: they contain both numerical and cat... read more 
Machine Learning cs.AI

CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration

arXiv Mar 24, 2026
Medical image registration is a fundamental task in medical image analysis, enabling the alignment of images from different modalities or time points. However, intensity inconsistencies and nonlinear tissue deformations pose significant challenges to... read more 
Computer Vision and Pattern Recognition

AdvSplat: Adversarial Attacks on Feed-Forward Gaussian Splatting Models

arXiv Mar 24, 2026
3D Gaussian Splatting (3DGS) is increasingly recognized as a powerful paradigm for real-time, high-fidelity 3D reconstruction. However, its per-scene optimization pipeline limits scalability and generalization, and prevents efficient inference. Recen... read more 
Computer Vision and Pattern Recognition

Integration of Multiple Optimization Algorithms with Machine Learning: Predicting Nitrogen Adsorption Volume of Coal Rocks.

Langmuir : the ACS journal of surfaces and colloids Mar 24, 2026
The nitrogen adsorption capacity is of great significance for the characterization of porous materials, carbon sequestration, and coalbed methane development. Essentially, this is a typical research scenario of solid-gas interface interaction in the ... read more 

What factors influence patient participation in an artificial intelligence-based initiative to optimise referrals from primary to specialist haematology care? A multicentre retrospective observational study in four Spanish hospitals.

BMJ open Mar 24, 2026
OBJECTIVES: Increasing demand for haematological specialist care makes the optimisation of referrals and outpatient workflow a priority. Automated placing of standardised test orders prior to the first appointment may provide haematologists with nece... read more 

Artificial Intelligence in Neurocritical Care : Multimodal Biosignal Analysis for Prognosis, Monitoring, and Future Pediatric Applications.

Journal of Korean Neurosurgical Society Mar 24, 2026
Neurocritical care relies on continuous assessment of neurological function and physiology under time pressure, yet bedside teams must interpret high-frequency, multimodal data that include hemodynamic waveforms, intracranial pressure (ICP), electroe... read more 
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