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Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection Fraction
Open accessAbstract
Introduction. Point-of-care ultrasound (POCUS) use is now universal among nonexperts. Artificial intelligence (AI) is currently employed by nonexperts in various imaging modalities to assist in diagnosis and decision making. Aim. To evaluate the diagnostic accuracy of POCUS, operated by medical students with the assistance of an AI-based tool for assessing the left ventricular ejection fraction (LVEF) of patients admitted to a cardiology department. Methods. Eight students underwent a 6-hour didactic and hands-on training session. Participants used a hand-held ultrasound device (HUD) equipped with an AI-based tool for the automatic evaluation of LVEF. The clips were assessed for LVEF by three methods: visually by the students, by students + the AI-based tool, and by the cardiologists. All LVEF measurements were compared to formal echocardiography completed within 24 hours and were evaluated for LVEF using the Simpson method and eyeballing assessment by expert echocardiographers. Results. The study included 88 patients (aged 58.3 ± 16.3 years). The AI-based tool measurement was unsuccessful in 6 cases. Comparing LVEF reported by students’ visual evaluation and students + AI vs. cardiologists revealed a correlation of 0.51 and 0.83, respectively. Comparing these three evaluation methods with the echocardiographers revealed a moderate/substantial agreement for the students + AI and cardiologists but only a fair agreement for the students’ visual evaluation. Conclusion. Medical students’ utilization of an AI-based tool with a HUD for LVEF assessment achieved a level of accuracy similar to that of cardiologists. Furthermore, the use of AI by the students achieved moderate to substantial inter-rater reliability with expert echocardiographers’ evaluation.
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In cardiology, AI-assisted teaching has been used to help learn how to perform echocardiography and to evaluate the determination of left ventricular ejection fraction (LVEF); AI helped accelerate progression from novice to expert with ultrasound use.
“In cardiology, AI-assisted teaching has been used to help learn how to perform echocardiography [91, 92] and to evaluate the determination of left ventricular ejection fraction (LVEF) [93]. In addition, AI helped accelerate progression from novice to expert with ultrasound use [93].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [93]
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Artificial intelligence in undergraduate medical education: an updated scoping review
Simoni, J., Urtubia-Fernandez, J., Mengual, E., Simoni, D. A., Royo, M., Egaña-Yin, D., Hertog, O. L. A., López-Ortiz, L., Muñoz-Tomás, A., Santiago-Martínez, P., et al. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08188-2 · Cited by 62
“In cardiology, AI-assisted teaching has been used to help learn how to perform echocardiography [91, 92] and to evaluate the determination of left ventricular ejection fraction (LVEF) [93].”
Metadata
- Authors
- Dadon, Z., Orlev, A., Butnaru, A., Rosenmann, D., Glikson, M., Gottlieb, S., & Alpert, E. A.
- Affiliations
- Hebrew University of Jerusalem (IL)
- Shaare Zedek Medical Center (IL)
- Tel Aviv University (IL)
- Corresponding author
- Dadon, Ziv
- Venue
- International Journal of Clinical Practice
- Publisher
- Wiley
- Published
- 2023-11-29
- Volume
- 2023
- Pages
- 1-9
- ISSN
- 1368-5031 (ISSN-L)
- 1742-1241
- PMC
- PMC10699938
- OpenAlex
- W4389131563
- Cited by
- 17
- References
- 30
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 3
- FWCI
- 3.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- International Journal of Clinical Practice (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Ultrasound in Clinical Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Function and Risk Factors
- Keywords
- point-of-care ultrasound, left ventricular ejection fraction, artificial intelligence, medical students, diagnostic accuracy, inter-rater reliability, ultrasound training
- MeSH terms
- Artificial Intelligence, Echocardiography, Humans, Power, Psychological, Stroke Volume, Students, Medical, Reproducibility of Results, Ventricular Function, Left, Point-of-Care Systems
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