Artificial intelligence-based automated interpretation of images of electrocardiograms: development and multinational validation of ECG-GPT
We developed and validated a vision encoder-decoder model that generates expert-level interpretations from ECG images, a scalable strategy for accessible automated ECG analysis.
Authors
- Bobak J. Mortazavi (ORCID: https://orcid.org/0000-0002-2655-2095)
- Sadeer G Al-Kindi (ORCID: https://orcid.org/0000-0002-1122-7695)
- Elijah Rockers (ORCID: https://orcid.org/0000-0003-1488-7661)
- Veer Sangha (ORCID: https://orcid.org/0000-0002-8524-1203)
- Akhil Vaid
- Akshay Khunte (ORCID: https://orcid.org/0000-0003-3812-3260)
- Sumukh Vasisht Shankar (ORCID: https://orcid.org/0009-0005-3034-0641)
- Andreas Coppi
- Rohan Khera
- Arya Aminorroaya
- E K Oikonomou
- L S Dhingra
- Girish N Nadkarni
- Deepak L Bhatt
- Harlan M. Krumholz
Institutions
- Houston Methodist (US)
- Yale New Haven Hospital (US)
- Hasso Plattner Institute (DE)
- Yale University (US)
- Mount Sinai Hospital (US)
- Yale New Haven Health System (US)
- Science Oxford (GB)
- New York University (US)
- Texas A&M University (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2026-02-18
- DOI
- https://doi.org/10.1093/ehjdh/ztag031
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00