Zobrazeno 1 - 10
of 954
pro vyhledávání: '"Rajpurkar, A."'
Autor:
Rajpurkar, Pranav, Acosta, Julian N., Dogra, Siddhant, Jeong, Jaehwan, Jindal, Deepanshu, Moritz, Michael, Rajpurkar, Samir
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 study focuses on rigorous assessment of the model's performance and g
Externí odkaz:
http://arxiv.org/abs/2412.12629
Autor:
Acosta, Julián N., Dogra, Siddhant, Adithan, Subathra, Wu, Kay, Moritz, Michael, Kwak, Stephen, Rajpurkar, Pranav
Radiologists face increasing workload pressures amid growing imaging volumes, creating risks of burnout and delayed reporting times. While artificial intelligence (AI) based automated radiology report generation shows promise for reporting workflow o
Externí odkaz:
http://arxiv.org/abs/2412.12042
The increasing adoption of AI-generated radiology reports necessitates robust methods for detecting hallucinations--false or unfounded statements that could impact patient care. We present ReXTrust, a novel framework for fine-grained hallucination de
Externí odkaz:
http://arxiv.org/abs/2412.15264
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-conditioned autocorrection of inaccuracies within these reports. Using the
Externí odkaz:
http://arxiv.org/abs/2412.02971
Medical vision-language model models often struggle with generating accurate quantitative measurements in radiology reports, leading to hallucinations that undermine clinical reliability. We introduce FactCheXcker, a modular framework that de-halluci
Externí odkaz:
http://arxiv.org/abs/2411.18672
Autor:
Zhang, Xiaoman, Zhou, Hong-Yu, Yang, Xiaoli, Banerjee, Oishi, Acosta, Julián N., Miller, Josh, Huang, Ouwen, Rajpurkar, Pranav
AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardized benchmark for objectively evaluating their performance. To address this, we present ReXrank, https:
Externí odkaz:
http://arxiv.org/abs/2411.15122
Autor:
Zhang, Serena, Sambara, Sraavya, Banerjee, Oishi, Acosta, Julian, Fahrner, L. John, Rajpurkar, Pranav
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 to generating hallucinations, potentially compromising patient care. We
Externí odkaz:
http://arxiv.org/abs/2411.00299
A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challenges
Autor:
Wang, Zifeng, Wang, Hanyin, Danek, Benjamin, Li, Ying, Mack, Christina, Poon, Hoifung, Wang, Yajuan, Rajpurkar, Pranav, Sun, Jimeng
The integration of Large Language Models (LLMs) into medical applications has sparked widespread interest across the healthcare industry, from drug discovery and development to clinical decision support, assisting telemedicine, medical devices, and h
Externí odkaz:
http://arxiv.org/abs/2411.00024
Autor:
Luo, Luyang, Vairavamurthy, Jenanan, Zhang, Xiaoman, Kumar, Abhinav, Ter-Oganesyan, Ramon R., Schroff, Stuart T., Shilo, Dan, Hossain, Rydhwana, Moritz, Mike, Rajpurkar, Pranav
Radiology reports, designed for efficient communication between medical experts, often remain incomprehensible to patients. This inaccessibility could potentially lead to anxiety, decreased engagement in treatment decisions, and poorer health outcome
Externí odkaz:
http://arxiv.org/abs/2410.00441
Autor:
Acosta, Julián N., Zhang, Xiaoman, Dogra, Siddhant, Zhou, Hong-Yu, Payabvash, Seyedmehdi, Falcone, Guido J., Oermann, Eric K., Rajpurkar, Pranav
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-ONE enhances current information extraction derived metrics (such as
Externí odkaz:
http://arxiv.org/abs/2409.13038