arXiv AI
Jun 9
MedVision: Benchmarking Quantitative Medical Image Analysis
arXiv:2511. 18676v2 Announce Type: replace-cross Abstract: Current vision-language models (VLMs) in medicine are primarily designed for categorical question answering (e.
Scalable Drift Monitoring in Medical Imaging AI
arXiv:2410. 13174v3 Announce Type: replace-cross Abstract: The integration of artificial intelligence (AI) into medical imaging has advanced clinical diagnostics but poses challenges in managing model drift and ensuring long-term reliability.
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arXiv AI
Jun 24
A global log for medical AI
arXiv:2510. 04033v2 Announce Type: replace Abstract: Modern computer systems rely on syslog, a universal protocol that records critical events across heterogeneous infrastructure.
arXiv AI
Jul 3
MedStreamBench: A Time-Aware Benchmark for Streaming and Proactive Medical Video Understanding
arXiv:2607. 01751v1 Announce Type: cross Abstract: Existing medical video benchmarks primarily evaluate whether a model produces the correct answer, but rarely assess whether it answers at the right time.
arXiv Machine Learning
Jul 30
AI Alignment in Medical Imaging: Unveiling Hidden Biases Through Counterfactual Analysis
arXiv:2504. 19621v2 Announce Type: replace Abstract: Machine learning (ML) systems for medical imaging have demonstrated remarkable diagnostic capabilities, but their susceptibility to biases poses significant risks, since biases may negatively impact generalization performance.
arXiv Machine Learning
Jul 14
Multimodal Routing for Interpretable, Robust, and Auditable Clinical Prediction
arXiv:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.
arXiv Machine Learning
Jun 16
Navigating Distribution Shifts in Medical Image Analysis: A Survey
arXiv:2411. 05824v4 Announce Type: replace-cross Abstract: Medical Image Analysis (MedIA) has become indispensable in modern healthcare, enhancing clinical diagnostics and personalized treatment.
arXiv AI
Jun 30
Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approach
arXiv:2604. 19191v2 Announce Type: replace-cross Abstract: Deploying AI-based anomaly detection across diverse clinical imaging settings remains challenging because most existing methods rely on modality-specific architectures, anatomical priors, or extensive retraining, limiting their use as general-purpose screening tools.
arXiv Machine Learning
Aug 12
VIDS-Seg: Towards Reliable Uncertainty Quantification in Pediatric Cardiac Ultrasound Segmentation
arXiv:2608. 10903v1 Announce Type: cross Abstract: Reliable clinical deployment of machine learning requires models that know when they are likely to fail, particularly for subgroups underrepresented in training data.
arXiv AI
Jul 8
The Large Cancer Assistant (LCA): A Model-Agnostic Orchestration Framework for Scalable Clinical Decision Support in Oncology
arXiv:2607. 06531v1 Announce Type: new Abstract: - Objective: Multimodal deep learning models in oncology are currently limited by monolithic designs that rigidly couple data ingestion, clinical routing, and artificial intelligence (AI) inference.
arXiv Machine Learning
3d ago
Mr.Dec: Daily-Scale Longitudinal Multimodal Modeling for 30-Day Readmission Prediction
Mr.Dec is a new Transformer‑decoder model that predicts 30‑day hospital readmission by treating each admission as a chronological sequence of daily multimodal events, integrating Electronic Health Record updates and Chest X‑ray findings. It uses disease‑specific supervised contrastive learning to shape a diagnosis‑aware latent space and preserves day‑level clinical signals that other methods often compress. Experiments on MIMIC‑IV and MIMIC‑CXR datasets show state‑of‑the‑art performance and the model can highlight "Critical Days" for actionable real‑time risk stratification.
arXiv AI
Jul 29
OPERA: Offline Policy-guided Expert Routing and Adaptation for Universal Biomedical Image Analysis
arXiv:2607. 25108v1 Announce Type: cross Abstract: Biomedical image analysis spans diverse modalities and tasks, yet real-world deployment is hindered by severe distribution shifts across scanners, protocols, and patient populations.
arXiv AI
Jun 8
ReclAIm: A Multi-Agent Framework for Monitoring and Correcting Performance Decline in Medical Imaging AI
arXiv:2510. 17004v2 Announce Type: replace-cross Abstract: Purpose: To develop and evaluate a multi-agent framework (ReclAIm) for automated monitoring, detection, and correction of performance decline in medical image classification models.