AXpert: human expert facilitated privacy-preserving large language models for abdominal X-ray report labeling
AXpert demonstrates potential to reduce human labeling efforts while maintaining high accuracy in automating NEC diagnosis with AXR, offering precise image labeling capabilities.
Authors
- Emily Wittrup (ORCID: https://orcid.org/0000-0002-5314-5903)
- Katelyn Webster (ORCID: https://orcid.org/0000-0002-8443-3115)
- Joseph G. Kohne (ORCID: https://orcid.org/0000-0001-9978-2759)
- Rebecca Vartanian (ORCID: https://orcid.org/0000-0001-9382-1219)
- Kayvan Najarian (ORCID: https://orcid.org/0000-0003-4485-6612)
- Yufeng Zhang (ORCID: https://orcid.org/0000-0003-3293-4759)
Institutions
- University of Michigan (US)
Publication Details
- Journal
- JAMIA Open
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf008
- Citations
- 2
- Primary Topic
- Tracheal and airway disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.84
Funders
- National Science Foundation
- National Institutes of Health