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Generative AI for ECG Interpretation Education: Impact on Nursing, Student Performance, and AI Model Accuracy
Dillon J Dzikowicz1, Nikolas DiPaulo, Tara Serwetnyk
1Author Affiliations: School of Nursing, University of Rochester, Rochester, New York (Dr Dzikowicz, Mr DiPaulo, Dr Serwetnyk, Dr Marconi, and Dr Carey); University of Rochester Medical Center, Rochester, New York (Dr Dzikowicz); Clinical Cardiovascular Research Center, University of Rochester, Rochester, New York (Dr Dzikowicz); and Department of Arts and Sciences, University of Rochester, Rochester, New York (Mr DiPaulo).
Background:
Electrocardiogram (ECG) interpretation is a critical yet challenging skill for nurses. Generative artificial intelligence (AI) offers potential for personalized, adaptive learning.
Purpose:
The aim was to evaluate the effectiveness, accuracy, and cost-efficiency of AI models in nursing ECG education.
Methods:
A 2-part study compared 4 AI models (GoodNurse, ChatGPT-5, Claude Sonnet 4, Microsoft Copilot) on an 88-item ECG exam and assessed cost-effectiveness. GoodNurse was then integrated into a 4-credit ECG course; AI usage, satisfaction, and grades were analyzed.
Results:
Accuracy varied (P <.01): GoodNurse 85.3%, ChatGPT-5 83.1%, Copilot 80.9%, Sonnet 79.1%. GoodNurse had the fewest waveform errors and the best cost-per-accuracy ($8.06 per 1% gain). In the course, 43% of students used GoodNurse, achieving higher grades (95.1 ± 2.5%) than nonusers (88.8 ± 5.9%; P =.0048), with a $5.80 per 1% grade improvement.
Conclusion:
Domain-specific AI, such as GoodNurse, enhances ECG learning, diagnostic accuracy, and cost-efficiency, supporting its integration into nursing education.
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