Enhancing Coronary Revascularization Decisions: The Promising Role of Large Language Models as a Decision-Support Tool for Multidisciplinary Heart Team
The present study demonstrates that advanced language learning models like ChatGPT-4 may be able to predict clinical recommendations for coronary artery disease revascularization with reasonable accuracy, especially in specific patient groups, underscoring their potential role as a supportive tool in clinical decision-making.
Authors
- Roni Ramon‐Gonen (ORCID: https://orcid.org/0000-0002-2529-1567)
- Eyal Klang (ORCID: https://orcid.org/0000-0002-4567-3108)
- Zachi Itzhak Attia (ORCID: https://orcid.org/0000-0002-9706-7900)
- Paul Fefer (ORCID: https://orcid.org/0000-0003-3632-8908)
- Sergei Amunts (ORCID: https://orcid.org/0000-0001-8108-8099)
- Israel M. Barbash (ORCID: https://orcid.org/0000-0002-7416-5989)
- Amitai Segev (ORCID: https://orcid.org/0000-0002-6653-7543)
- Mohamad Adnan Alkhouli (ORCID: https://orcid.org/0000-0003-3847-0959)
- Noam Barda (ORCID: https://orcid.org/0000-0002-3400-235X)
- Karin Sudri (ORCID: https://orcid.org/0000-0002-5118-8619)
- Michal Cohen-Shelly (ORCID: https://orcid.org/0009-0007-6532-9252)
- Iris Motro-Feingold
Institutions
- Ben-Gurion University of the Negev (IL)
- Mayo Clinic (US)
- Bar-Ilan University (IL)
- Tel Aviv University (IL)
- Sheba Medical Center (IL)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Circulation Cardiovascular Interventions
- Published
- 2024-11-01
- DOI
- https://doi.org/10.1161/circinterventions.124.014201
- Citations
- 11
- Primary Topic
- Cardiac, Anesthesia and Surgical Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 4.42