Automated identification of incidental hepatic steatosis on Emergency Department imaging using large language models.
Large language models can assist in identifying incidental conditions from imaging reports that otherwise may be missed opportunities for early disease intervention. Large language models are a democratization of natural language processing by allowing for a user-friendly, expansive analyses of electronic medical records without requiring the development of complex natural language processing models.
Authors
- Alexandra T. Strauss (ORCID: https://orcid.org/0000-0001-6313-7221)
- Jeremiah S. Hinson (ORCID: https://orcid.org/0000-0002-2024-2360)
- Eili Klein (ORCID: https://orcid.org/0000-0002-1304-5289)
- Tyrus Vong (ORCID: https://orcid.org/0000-0001-8133-9150)
- Mark Dredze (ORCID: https://orcid.org/0000-0002-0422-2474)
- Tinsay A. Woreta (ORCID: https://orcid.org/0000-0001-7292-4518)
- Vedant Jain
- Valerie Thompson (ORCID: https://orcid.org/0000-0001-6502-8600)
- Nicholas Rizer (ORCID: https://orcid.org/0000-0002-8854-5755)
- Tanjala S. Purnell (ORCID: https://orcid.org/0000-0002-8661-7911)
- Stephen Kwak
Institutions
- Johns Hopkins University (US)
- Johns Hopkins Medicine (US)
Publication Details
- Journal
- PubMed
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1097/hc9.0000000000000638
- Citations
- 2
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.71