Large Language Modeling–Enabled Analysis of Atrial Fibrillation on Social Media
The artificial intelligence-enabled analysis underscored substantial public discourse around patient experiences with AF detection and management. Leveraging social media data to understand patient perspectives on cardiovascular health may inform patient-centered resources and future research directions to better support patients living with AF.
Authors
- Shyon Parsa (ORCID: https://orcid.org/0000-0002-3909-154X)
- Albert J. Rogers (ORCID: https://orcid.org/0000-0001-6585-534X)
- Tina Hernandez‐Boussard (ORCID: https://orcid.org/0000-0001-6553-3455)
- Fátima Rodríguez (ORCID: https://orcid.org/0000-0002-5226-0723)
- Sulaiman Somani (ORCID: https://orcid.org/0000-0003-0913-8674)
- Sneha S. Jain (ORCID: https://orcid.org/0000-0002-0592-1453)
Institutions
- Cardiovascular Institute of the South (US)
- Stanford Health Care (US)
- Stanford Medicine (US)
- Hospital Universitario San Ignacio (CO)
- Stanford University (US)
Publication Details
- Journal
- Journal of the American Heart Association
- Published
- 2025-12-17
- DOI
- https://doi.org/10.1161/jaha.125.043999
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00