Unveiling patterns in clinical data: exploring the role of large language models and clustering algorithms
LLMs can be effectively leveraged to repurpose existing clinical datasets for individualized clinical questions, such as optimizing surgical timing for patients with infective endocarditis and embolic stroke. This approach advances precision medicine and supports data-driven clinical decision-making.
Authors
- Syed H. Jafri (ORCID: https://orcid.org/0009-0001-8505-4109)
- Subi Gandhi (ORCID: https://orcid.org/0000-0003-0269-2082)
- Mohammed M. Ali (ORCID: https://orcid.org/0000-0002-1744-0833)
- J Hunter Mehaffey
- Syed Y. Raza
- Abbas S. Ali
- Sulaiman Samian
Institutions
- University of Central Florida (US)
- West Virginia University (US)
- Kern Medical Center (US)
- Bon Secours Heart & Vascular Institute (US)
- Tarleton State University (US)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2026-03-09
- DOI
- https://doi.org/10.3389/frai.2026.1737530
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00