Physiological foundation modeling for subclinical disease assessment: a prospective pilot
Artificial intelligence (AI)-based profiling of patient physiology can reveal individuals who have subclinical signatures of disease. If implemented widely, this approach can identify unknown human subjects, reduce screening failures, and improve trial quality. Bioprofiles may have additional utility in decision support applications in precision medicine and AI-augmented healthcare.
Authors
- Charles R. Flynn (ORCID: https://orcid.org/0000-0002-3749-0598)
- William Yuan
- Sara Dionisi (ORCID: https://orcid.org/0000-0001-7933-8490)
- Katharine M. von Herrmann (ORCID: https://orcid.org/0000-0002-9736-2766)
- Joyce Cheung‐Flynn (ORCID: https://orcid.org/0000-0002-5369-9244)
- Scott Lipnick (ORCID: https://orcid.org/0000-0002-4271-746X)
- Caleb Kennedy
- Kate Frederick-Dyer
- Shiwei Xu
Institutions
- Etiometry (United States) (US)
- Flagship Pioneering (United States) (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- JAMIA Open
- Published
- 2026-01-06
- DOI
- https://doi.org/10.1093/jamiaopen/ooag002
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00