85 citations · 565 across the 57 of their papers we have counts for
60 papers
ProTok: Unified Protein Tokenization via Modular Multi-Codebook Quantization
Romain Hardy, Xiaoman Zhang, Pranav Rajpurkar
Protein sequence, structure, and functional annotations each capture complementary aspects of protein biology, yet existing tokenizers encode each modality independently, limiting…
Asclepius: An Adaptive Harness for Long-Horizon Clinical Agents
Grace Chang Yuan, Xiaoman Zhang, Sung Eun Kim +2
LLM agents are predominantly benchmarked on short, single-task trajectories, yet real deployments run for hours under contention, surfacing a different class of failures. We use th…
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…
Evaluating Contextual Intelligence in Recyclability: A Comprehensive Study of Image-Based Reasoning Systems
Eliot Park, Abhi Kumar, Pranav Rajpurkar
While the importance of efficient recycling is widely acknowledged, accurately determining the recyclability of items and their proper disposal remains a complex task for the gener…
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…
ColonCrafter: A Depth Estimation Model for Colonoscopy Videos Using Diffusion Priors
Romain Hardy, Tyler Berzin, Pranav Rajpurkar
Three-dimensional (3D) scene understanding in colonoscopy presents significant challenges that necessitate automated methods for accurate depth estimation. However, existing depth…
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…
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…
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…
a2z-1 for Multi-Disease Detection in Abdomen-Pelvis CT: External Validation and Performance Analysis Across 21 Conditions
Pranav Rajpurkar, Julian N. Acosta, Siddhant Dogra +4
We present a comprehensive evaluation of a2z-1, an artificial intelligence (AI) model designed to analyze abdomen-pelvis CT scans for 21 time-sensitive and actionable findings. Our…
The Impact of AI Assistance on Radiology Reporting: A Pilot Study Using Simulated AI Draft Reports
Julián N. Acosta, Siddhant Dogra, Subathra Adithan +4
Radiologists face increasing workload pressures amid growing imaging volumes, creating risks of burnout and delayed reporting times. While artificial intelligence (AI) based automa…
MedAutoCorrect: Image-Conditioned Autocorrection in Medical Reporting
Arnold Caleb Asiimwe, Dídac Surís, Pranav Rajpurkar +1
In medical reporting, the accuracy of radiological reports, whether generated by humans or machine learning algorithms, is critical. We tackle a new task in this paper: image-condi…
RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models
Serena Zhang, Sraavya Sambara, Oishi Banerjee +3
Generating accurate radiology reports from medical images is a clinically important but challenging task. While current Vision Language Models (VLMs) show promise, they are prone t…
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…
ReXrank: A Public Leaderboard for AI-Powered Radiology Report Generation
Xiaoman Zhang, Hong-Yu Zhou, Xiaoli Yang +6
AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardized benchmark for objectively eva…
HeadCT-ONE: Enabling Granular and Controllable Automated Evaluation of Head CT Radiology Report Generation
Julián N. Acosta, Xiaoman Zhang, Siddhant Dogra +5
We present Head CT Ontology Normalized Evaluation (HeadCT-ONE), a metric for evaluating head CT report generation through ontology-normalized entity and relation extraction. HeadCT…