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 Machine Learning
Oct 2
Data-Driven Priors for Uncertainty-Aware Risk Prediction of Clinical Deterioration using Multimodal Data
The paper introduces MedCertAIn, a framework that uses data‑driven priors to enhance uncertainty estimation in multimodal clinical models. By integrating cross‑modal similarity and modality‑specific corruptions into neural network priors, the authors improve predictive performance and selective prediction for in‑hospital mortality risk using MIMIC‑IV and MIMIC‑CXR data. The results demonstrate competitive accuracy and notable gains over deterministic and stochastic baselines, underscoring the potential of such priors for reliable, uncertainty‑aware clinical decision support.
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 AI
Oct 12
WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Care
WoundAmbit is a framework that evaluates state‑of‑the‑art deep learning models for wound segmentation, benchmarking general‑purpose vision, medical imaging, and public wound challenge methods under standardized training, augmentation, and cross‑validation. The study finds transformer‑based TransNeXt offers the best generalizability, while all models run at least one image per second on a CPU and show strong interpretability and expert‑approved mask quality. The framework also introduces a reference‑object approach to convert masks into clinically relevant wound size estimates, demonstrating close alignment with expert annotations and integration into a custom telehealth system.
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 Computation and Language
Sep 7
CT-$\Delta$Bench: A Benchmark for Longitudinal 3D Medical Imaging Difference Reporting with Vision-Language Models
CT‑ΔBench is a new benchmark designed to evaluate vision‑language models on longitudinal 3D medical imaging difference reporting. It provides patient‑level split data, change‑aware metrics, and physician‑validated references to assess clinically meaningful interval changes between two CT scans. The paper also introduces DeltaMed, a baseline model that directly reasons over paired CT scans, and compares it to an indirect two‑stage approach that first generates single‑timepoint reports before differencing.
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.