47 citations · 102 across the 11 of their papers we have counts for
11 papers
ReXamine-Global: A Framework for Uncovering Inconsistencies in Radiology Report Generation Metrics
Oishi Banerjee, Agustina Saenz, Kay Wu +17
Given the rapidly expanding capabilities of generative AI models for radiology, there is a need for robust metrics that can accurately measure the quality of AI-generated radiology…
Uncovering Knowledge Gaps in Radiology Report Generation Models through Knowledge Graphs
Xiaoman Zhang, Julián N. Acosta, Hong-Yu Zhou +1
Recent advancements in artificial intelligence have significantly improved the automatic generation of radiology reports. However, existing evaluation methods fail to reveal the mo…
Direct Preference Optimization for Suppressing Hallucinated Prior Exams in Radiology Report Generation
Oishi Banerjee, Hong-Yu Zhou, Subathra Adithan +3
Recent advances in generative vision-language models (VLMs) have exciting potential implications for AI in radiology, yet VLMs are also known to produce hallucinations, nonsensical…
Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
Emma Chen, Aman Kansal, Julie Chen +4
We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a comprehensive benchmark for evaluating foundation models in Emergency Medicine using a dataset of 100K+…
Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting
Benjamin Yan, Ruochen Liu, David E. Kuo +8
Automatically generated reports from medical images promise to improve the workflow of radiologists. Existing methods consider an image-to-report modeling task by directly generati…
Exploring the Boundaries of GPT-4 in Radiology
Qianchu Liu, Stephanie Hyland, Shruthi Bannur +16
The recent success of general-domain large language models (LLMs) has significantly changed the natural language processing paradigm towards a unified foundation model across domai…