Work

BRIDGE: benchmarking large language models for understanding real-world clinical practice texts

Year: 2026

Type: article

Source: Nature Biomedical Engineering

DOI: 10.1038/s41551-026-01719-2

Authors: Jiageng Wu, Bowen Gu, Ren Zhou, Kevin Xie, Doug Snyder, Yixing Jiang, Valentina Carducci, Richard Wyss, Rishi J. Desai, Emily Alsentzer, Leo Anthony Celi, Adam Rodman, Sebastian Schneeweiss, Jonathan H. Chen, Santiago Romero-Brufau, Kueiyu Joshua Lin, Jie Yang

Institutions: Brigham and Women's Hospital, Harvard University, University of Illinois Urbana-Champaign, Massachusetts Institute of Technology, Mayo Clinic, Stanford University, Beth Israel Deaconess Medical Center, Stanford Medicine, Broad Institute

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: closed

Funders: Patient-Centered Outcomes Research Institute, National Institutes of Health, Brigham Research Institute, National Institute on Aging, U.S. National Library of Medicine

Awards: G2847480730, Stanford Center for Clinical and Translational Research and Education, Optimizing validity of comparative effectiveness research in Alzheimer's disease and related dementias using large language models, Developing large language models for drug safety and effectiveness causal analysis

OpenAlex ID: W7165053112

FWCI: 13.77

Cites: 80

Cited by: 7

Related to: 10