3.5k citations
- National Institutes of HealthUS71 papers
- National Institutes of Health Clinical CenterUS7 papers
- United States National Library of MedicineUS7 papers
- Radboud University NijmegenNL6 papers
- University of Maryland, College ParkUS5 papers
- Cornell UniversityUS4 papers
- Howard UniversityUS4 papers
- University of FribourgCH4 papers
- Weill Cornell MedicineUS4 papers
- Institute for Advanced StudyUS3 papers
- National Eye InstituteUS3 papers
- University of Illinois Urbana-ChampaignUS3 papers
91 papers
Fold-switching Proteins
Devlina Chakravarty, Lauren L. Porter
Globular proteins are expected to assume folds with fixed secondary structures, alpha-helices and beta-sheets. Fold-switching proteins challenge this expectation by remodeling thei…
Toward Relieving Clinician Burden by Automatically Generating Progress Notes using Interim Hospital Data
Sarvesh Soni, Dina Demner-Fushman
Regular documentation of progress notes is one of the main contributors to clinician burden. The abundance of structured chart information in medical records further exacerbates th…
Augmenting Biomedical Named Entity Recognition with General-domain Resources
Yu Yin, Hyunjae Kim, Xiao Xiao +6
Training a neural network-based biomedical named entity recognition (BioNER) model usually requires extensive and costly human annotations. While several studies have employed mult…
Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology Reports
Qingqing Zhu, Xiuying Chen, Qiao Jin +6
In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging. Current metrics,…
A survey of recent methods for addressing AI fairness and bias in biomedicine
Yifan Yang, Mingquan Lin, Han Zhao +3
Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reduci…
Unmasking and Quantifying Racial Bias of Large Language Models in Medical Report Generation
Yifan Yang, Xiaoyu Liu, Qiao Jin +2
Large language models like GPT-3.5-turbo and GPT-4 hold promise for healthcare professionals, but they may inadvertently inherit biases during their training, potentially affecting…