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Electrocardiography (ECG) offers a cost-effective and accessible alternative for estimating LVEF. However, specialized AI models for this purpose are often complex and costly to develop.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study uniquely evaluates GPT-4o Fine-Tuned Vision (GPT-4o-FTV), a general-purpose AI model, for detecting LVEF\u2009\u2264\u200935% from ECG images, comparing its performance with a Convolutional Neural Network (CNN) model and clinician assessments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We analyzed ECGs from 202 patients (42.6% women, mean age 64.5\u2009\u00b1\u200916.3 years) at a tertiary center, excluding those with pacemakers and including only high-quality ECGs. LVEF\u2009\u2264\u200935% was present in 11.9% (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200924). GPT-4o-FTV, trained on 20 labeled ECGs, was tested using a structured prompt across four runs. Accuracy, sensitivity, specificity, and positive predictive value (PPV) were compared to a CNN model and four clinicians.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>GPT-4o-FTV achieved 79.9% accuracy, 72.9% sensitivity, 80.8% specificity, an F1-score of 46.4%, and a PPV of 34%, outperforming clinicians (74.9% accuracy, 65.6% sensitivity, 76.1% specificity, 39% F1-score, PPV 27.9%). The CNN model had the highest performance (89.1% accuracy, 79.2% sensitivity, 90.4% specificity, 63.3% F1-score, PPV 52.8%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10916-025-02289-7","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T07:48:33Z","timestamp":1762760913000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Estimating LVEF from ECG with GPT-4o Fine-Tuned Vision: A Novel Approach in AI-Driven Cardiac Diagnostics"],"prefix":"10.1007","volume":"49","author":[{"given":"Haya","family":"Engelstein","sequence":"first","affiliation":[]},{"given":"Roni","family":"Ramon-Gonen","sequence":"additional","affiliation":[]},{"given":"Israel","family":"Barbash","sequence":"additional","affiliation":[]},{"given":"Roy","family":"Beinart","sequence":"additional","affiliation":[]},{"given":"Michal","family":"Cohen-Shelly","sequence":"additional","affiliation":[]},{"given":"Avi","family":"Sabbag","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"issue":"5","key":"2289_CR1","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1159\/000505148","volume":"145","author":"P D\u00edez-Villanueva","year":"2020","unstructured":"D\u00edez-Villanueva P, Vicent L, de la Cuerda F, et al. 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