Work

Automated identification of incidentalomas requiring follow-up: A multi-anatomy evaluation of LLM-based and supervised approaches

Year: 2026

Type: article

Source: Journal of Biomedical Informatics

DOI: 10.1016/j.jbi.2026.105048

Authors: Namu Park, Farzad Ahmed, Zhaoyi Sun, Kevin Lybarger, Ethan Breinhorst, Julie Hu, Özlem Uzuner, Martin Gunn, Meliha Yetisgen

Institutions: University of Washington, George Mason University, Health New Zealand

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute, National Center for Advancing Translational Sciences

Awards: G5067454938, G691201058, Large scale clinical and economic impact analysis of potentially malignant incidental findings in radiology reports, NRSA Training Core, Institutional Career Development Core

OpenAlex ID: W4417142995

FWCI:

Cites:

Cited by:

Related to: 10