1 citations · 4 across the 12 of their papers we have counts for
12 papers
Imitation Learning for Robot Assistance in Open Surgery: A Multi-Policy Evaluation on Suture Following
Xucheng Wang, Zhizhou Yang, Xiaoman Zhang +3
This study presents the first evaluation of general-purpose imitation learning for surgeon-robot collaborative assistance in open surgery, targeting suture following: the grab-pull…
ReXSonoVQA: A Video QA Benchmark for Procedure-Centric Ultrasound Understanding
Xucheng Wang, Xiaoman Zhang, Sung Eun Kim +2
Ultrasound acquisition requires skilled probe manipulation and real-time adjustments. Vision-language models (VLMs) could enable autonomous ultrasound systems, but existing benchma…
ReXInTheWild: A Unified Benchmark for Medical Photograph Understanding
Oishi Banerjee, Sung Eun Kim, Alexandra N. Willauer +9
Everyday photographs taken with ordinary cameras are already widely used in telemedicine and other online health conversations, yet no comprehensive benchmark evaluates whether vis…
CRIMSON: A Clinically-Grounded LLM-Based Metric for Generative Radiology Report Evaluation
Mohammed Baharoon, Thibault Heintz, Siavash Raissi +6
We introduce CRIMSON, a clinically grounded evaluation framework for chest X-ray report generation that assesses reports based on diagnostic correctness, contextual relevance, and…
Do Mixed-Vendor Multi-Agent LLMs Improve Clinical Diagnosis?
Grace Chang Yuan, Xiaoman Zhang, Sung Eun Kim +1
Multi-agent large language model (LLM) systems have emerged as a promising approach for clinical diagnosis, leveraging collaboration among agents to refine medical reasoning. Howev…
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…
RadGame: An AI-Powered Platform for Radiology Education
Mohammed Baharoon, Siavash Raissi, John S. Jun +29
We introduce RadGame, an AI-powered gamified platform for radiology education that targets two core skills: localizing findings and generating reports. Traditional radiology traini…
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…
ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports
Romain Hardy, Sung Eun Kim, Du Hyun Ro +1
The increasing adoption of AI-generated radiology reports necessitates robust methods for detecting hallucinations--false or unfounded statements that could impact patient care. We…
FactCheXcker: Mitigating Measurement Hallucinations in Chest X-ray Report Generation Models
Alice Heiman, Xiaoman Zhang, Emma Chen +2
Medical vision-language models often struggle with generating accurate quantitative measurements in radiology reports, leading to hallucinations that undermine clinical reliability…
Supervised Segmentation with Domain Adaptation for Small Sampled Orbital CT Images
Sungho Suh, Sojeong Cheon, Wonseo Choi +6
Deep neural networks (DNNs) have been widely used for medical image analysis. However, the lack of access a to large-scale annotated dataset poses a great challenge, especially in…