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Related Experiment Videos

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).

Nurse Educator
|May 15, 2026
PubMed
Summary

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An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
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Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...

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Domain-specific artificial intelligence (AI) like GoodNurse significantly improves nursing students' electrocardiogram (ECG) diagnostic accuracy and learning outcomes, offering a cost-effective educational tool.

Area of Science:

  • Nursing Education
  • Medical Technology
  • Artificial Intelligence

Background:

  • Electrocardiogram (ECG) interpretation is a crucial but difficult skill for nurses.
  • Generative artificial intelligence (AI) presents opportunities for tailored, adaptive learning in healthcare education.

Purpose of the Study:

  • To assess the effectiveness, accuracy, and cost-efficiency of AI models for nursing ECG education.
  • To compare the performance of different AI models in ECG interpretation training.

Main Methods:

  • Four AI models (GoodNurse, ChatGPT-5, Claude Sonnet 4, Microsoft Copilot) were evaluated using an 88-item ECG exam.
  • Cost-effectiveness was analyzed, and GoodNurse was integrated into a nursing ECG course to assess its impact on student performance and satisfaction.
Keywords:
clinical competencedomain-specific AIeducational measurementnursing students

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Main Results:

  • AI model accuracy varied, with GoodNurse achieving the highest at 85.3%.
  • GoodNurse demonstrated superior cost-per-accuracy and was linked to higher student grades (95.1%) compared to non-users (88.8%) in the course.
  • AI integration showed a cost of $5.80 per 1% grade improvement.

Conclusions:

  • Domain-specific AI, exemplified by GoodNurse, enhances ECG learning and diagnostic accuracy in nursing.
  • The study supports the integration of specialized AI tools into nursing education for improved outcomes and cost-efficiency.