85 citations · 565 across the 57 of their papers we have counts for
10 papers · 1 filter
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…
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…
ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports
Vishwanatha M. Rao, Serena Zhang, Julian N. Acosta +2
Accurately interpreting medical images and writing radiology reports is a critical but challenging task in healthcare. Both human-written and AI-generated reports can contain error…
RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information Extraction
Sameer Khanna, Adam Dejl, Kibo Yoon +4
We present RadGraph2, a novel dataset for extracting information from radiology reports that focuses on capturing changes in disease state and device placement over time. We introd…
Multimodal Image-Text Matching Improves Retrieval-based Chest X-Ray Report Generation
Jaehwan Jeong, Katherine Tian, Andrew Li +7
Automated generation of clinically accurate radiology reports can improve patient care. Previous report generation methods that rely on image captioning models often generate incoh…
Improving Radiology Report Generation Systems by Removing Hallucinated References to Non-existent Priors
Vignav Ramesh, Nathan Andrew Chi, Pranav Rajpurkar
Current deep learning models trained to generate radiology reports from chest radiographs are capable of producing clinically accurate, clear, and actionable text that can advance…
Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management
Cécile Logé, Emily Ross, David Yaw Amoah Dadey +4
Recent advances in Natural Language Processing (NLP), and specifically automated Question Answering (QA) systems, have demonstrated both impressive linguistic fluency and a pernici…
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports
Saahil Jain, Ashwin Agrawal, Adriel Saporta +9
Extracting structured clinical information from free-text radiology reports can enable the use of radiology report information for a variety of critical healthcare applications. In…
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT
Akshay Smit, Saahil Jain, Pranav Rajpurkar +3
The extraction of labels from radiology text reports enables large-scale training of medical imaging models. Existing approaches to report labeling typically rely either on sophist…