Estimating LVEF from ECG with GPT-4o Fine-Tuned Vision: A Novel Approach in AI-Driven Cardiac Diagnostics
GPT-4o-FTV demonstrates strong potential as an accessible tool for cardiac diagnostics, particularly in resource-limited settings. While CNN models remain superior in accuracy, the ease of fine-tuning GPT-4o-FTV highlights its practical utility. Future research should focus on larger datasets, additional optimization, and exploring its ability to detect early predictors of LVEF decline.
Authors
- Roni Ramon‐Gonen (ORCID: https://orcid.org/0000-0002-2529-1567)
- Avi Sabbag (ORCID: https://orcid.org/0000-0003-4295-6679)
- Michal Cohen‐Shelly (ORCID: https://orcid.org/0000-0003-4817-8644)
- Haya Engelstein (ORCID: https://orcid.org/0009-0005-6721-5158)
- Israel M. Barbash (ORCID: https://orcid.org/0000-0002-7416-5989)
Institutions
- Bar-Ilan University (IL)
- Tel Aviv University (IL)
- Sheba Medical Center (IL)
Publication Details
- Journal
- Journal of Medical Systems
- Published
- 2025-11-10
- DOI
- https://doi.org/10.1007/s10916-025-02289-7
- Citations
- 1
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.39