2 citations · 6 across the 16 of their papers we have counts for
6 papers · 1 filter
ReX-MLE: The Autonomous Agent Benchmark for Medical Imaging Challenges
Roshan Kenia, Xiaoman Zhang, Pranav Rajpurkar
Autonomous coding agents built on large language models (LLMs) can now solve many general software and machine learning tasks, but they remain ineffective on complex, domain-specif…
3DReasonKnee: Advancing Grounded Reasoning in Medical Vision Language Models
Sraavya Sambara, Sung Eun Kim, Xiaoman Zhang +5
Current Vision-Language Models (VLMs) struggle to ground anatomical regions in 3D medical images and reason about them in a step-by-step manner, a key requirement of real-world dia…
ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports
Mohammed Baharoon, Luyang Luo, Michael Moritz +22
We introduce ReXGroundingCT, the first publicly available dataset linking free-text findings to pixel-level 3D segmentations in chest CT scans. The dataset includes 3,142 non-contr…
Voice-guided Orchestrated Intelligence for Clinical Evaluation (VOICE): A Voice AI Agent System for Prehospital Stroke Assessment
Julian Acosta, Scott Adams, Julius Kernbach +7
We developed a voice-driven artificial intelligence (AI) system that guides anyone - from paramedics to family members - through expert-level stroke evaluations using natural conve…
ReXVQA: A Large-scale Visual Question Answering Benchmark for Generalist Chest X-ray Understanding
Ankit Pal, Jung-Oh Lee, Xiaoman Zhang +5
We present ReXVQA, the largest and most comprehensive benchmark for visual question answering (VQA) in chest radiology, comprising approximately 696,000 questions paired with 160,0…
ReXGradient-160K: A Large-Scale Publicly Available Dataset of Chest Radiographs with Free-text Reports
Xiaoman Zhang, Julián N. Acosta, Josh Miller +2
We present ReXGradient-160K, representing the largest publicly available chest X-ray dataset to date in terms of the number of patients. This dataset contains 160,000 chest X-ray s…