Artificial Intelligence Medical Compendium

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

Showing 38,751 to 38,760 of 223,469 articles

Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction

arXiv
While large-scale pretraining has revolutionized language modeling, its potential remains underexplored in healthcare with structured electronic health records (EHRs). We present RAVEN, a novel generative pretraining strategy for sequential EHR data ... read more 

Anti-I2V: Safeguarding your photos from malicious image-to-video generation

arXiv
Advances in diffusion-based video generation models, while significantly improving human animation, poses threats of misuse through the creation of fake videos from a specific person's photo and text prompts. Recent efforts have focused on adversaria... read more 

Towards Training-Free Scene Text Editing

arXiv
Scene text editing seeks to modify textual content in natural images while maintaining visual realism and semantic consistency. Existing methods often require task-specific training or paired data, limiting their scalability and adaptability. In this... read more 

Vision-Language Models vs Human: Perceptual Image Quality Assessment

arXiv
Psychophysical experiments remain the most reliable approach for perceptual image quality assessment (IQA), yet their cost and limited scalability encourage automated approaches. We investigate whether Vision Language Models (VLMs) can approximate hu... read more 

Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving

arXiv
We introduce Latent-WAM, an efficient end-to-end autonomous driving framework that achieves strong trajectory planning through spatially-aware and dynamics-informed latent world representations. Existing world-model-based planners suffer from inadequ... read more 

Polynomial Speedup in Diffusion Models with the Multilevel Euler-Maruyama Method

arXiv
We introduce the Multilevel Euler-Maruyama (ML-EM) method compute solutions of SDEs and ODEs using a range of approximators $f^1,\dots,f^k$ to the drift $f$ with increasing accuracy and computational cost, only requiring a few evaluations of the most... read more 

A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data

arXiv
Single-cell RNA sequencing (scRNA-seq) is inherently affected by sparsity caused by dropout events, in which expressed genes are recorded as zeros due to technical limitations. These artifacts distort gene expression distributions and can compromise ... read more 

MedOpenClaw: Auditable Medical Imaging Agents Reasoning over Uncurated Full Studies

arXiv
Currently, evaluating vision-language models (VLMs) in medical imaging tasks oversimplifies clinical reality by relying on pre-selected 2D images that demand significant manual labor to curate. This setup misses the core challenge of realworld diagno... read more 

Spectral methods: crucial for machine learning, natural for quantum computers?

arXiv
This article presents an argument for why quantum computers could unlock new methods for machine learning. We argue that spectral methods, in particular those that learn, regularise, or otherwise manipulate the Fourier spectrum of a machine learning ... read more 

ReDiPrune: Relevance-Diversity Pre-Projection Token Pruning for Efficient Multimodal LLMs

arXiv
Recent multimodal large language models are computationally expensive because Transformers must process a large number of visual tokens. We present \textbf{ReDiPrune}, a training-free token pruning method applied before the vision-language projector,... read more