Large Language Models for Rare Disease Diagnosis at the Undiagnosed Diseases Network
This cohort study assesses whether large language models (LLMs) can identify the final diagnosis, based on available clinical information, for patients referred to the Undiagnosed Diseases Network.
Authors
- Peter J. Embí (ORCID: https://orcid.org/0000-0002-7733-0847)
- Lisa Bastarache
- Thomas Cassini (ORCID: https://orcid.org/0000-0002-3522-2135)
- Kevin Byram (ORCID: https://orcid.org/0000-0001-6441-1915)
- Rory J. Tinker (ORCID: https://orcid.org/0000-0001-7093-8844)
- Hua Xu (ORCID: https://orcid.org/0000-0002-5274-4672)
- Cathy Shyr (ORCID: https://orcid.org/0000-0001-7466-0034)
- Josh F. Peterson (ORCID: https://orcid.org/0000-0002-7553-0749)
- Rizwan Hamid (ORCID: https://orcid.org/0000-0001-5063-7614)
Institutions
- Yale University (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2025-08-22
- DOI
- https://doi.org/10.1001/jamanetworkopen.2025.28538
- Citations
- 8
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 5.23