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The effect of real-time EF automatic tool on cardiac ultrasound performance among medical students
Open accessAbstract
Purpose: Point-of-care ultrasound (POCUS) is a sensitive, safe, and efficient tool used in many clinical settings and is an essential part of medical education in the United States. Numerous studies present improved diagnostic performances and positive clinical outcomes among POCUS users. However, others stress the degree to which the modality is user-dependent, rendering high-quality POCUS training necessary in medical education. In this study, the authors aimed to investigate the potential of an artificial intelligence (AI) based quality indicator tool as a teaching device for cardiac POCUS performance. Methods: The authors integrated the quality indicator tool into the pre-clinical cardiac ultrasound course for 4th-year medical students and analyzed their performances. The analysis included 60 students who were assigned to one of two groups as follows: the intervention group using the AI-based quality indicator tool and the control group. Quality indicator users utilized the tool during both the course and the final test. At the end of the course, the authors tested the standard echocardiographic views, and an experienced clinician blindly graded the recorded clips. Results were analyzed and compared between the groups. Results: The results showed an advantage in quality indictor users’ median overall scores (P = 0.002) with a relative risk of 2.3 (95% CI: 1.10, 4.93, P = 0.03) for obtaining correct cardiac views. In addition, quality indicator users also had a statistically significant advantage in the overall image quality in various cardiac views. Conclusions: The AI-based quality indicator improved cardiac ultrasound performances among medical students who were trained with it compared to the control group, even in cardiac views in which the indicator was inactive. Performance scores, as well as image quality, were better in the AI-based group. Such tools can potentially enhance ultrasound training, warranting the expansion of the application to more views and prompting further studies on long-term learning effects.
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![Fig 1 The quality indicator tool. Quality indicator contour lines in green, yellow and red, correspond with good, medium and bad quality apical 4 chamber positioning. Republished from [12] under a CC BY license, with permission from GE HealthCare, original copyright 2021.](/paper-figures/ac8937f1e930485a8c2facadd160f6f8/the-effect-of-real-time-ef-automatic-tool-on-cardiac-ultrasound-performance-figure-1.jpg)
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“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 [91]
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Cites
Fast and accurate view classification of echocardiograms using deep learning
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Point-of-Care Ultrasound in Medical Education — Stop Listening and Look
Solomon, S. D., & Saldaña, F. (2014) · New England Journal of Medicine · doi:10.1056/nejmp1311944 · Cited by 284
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A Pilot Study of the Clinical Impact of Hand‐Carried Cardiac Ultrasound in the Medical Clinic
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Hand-Carried Ultrasound Improves the Bedside Cardiovascular Examination
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Point of care ultrasound for basic haemodynamic assessment: novice compared with an expert operator
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Cited by
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].”
Roveta, A., Castello, L. M., Massarino, C., Francese, A., Ugo, F., & Maconi, A. (2025) · AI · doi:10.3390/ai6090227 · Cited by 49
AI-Enhanced POCUS in Emergency Care
Puticiu, M. I., Cimpoesu, D. C., POP, F. B.-D., Ciumanghel, I., Rotaru, L. T., Opriță, B., Butoi, M. A., Belghiru, V. I., Tat, R. M., & Golea, A. C. (2026) · Diagnostics · doi:10.3390/diagnostics16020353 · Cited by 10
Lai, N. M., Lim, Y. S., Thein, W. M., Bhargava, P., Thomas, P., & Ong, Q. C. (2026) · JMIR Medical Education · doi:10.2196/88933 · Cited by 12
Comparative Evaluation of Self-Learning Versus Instructor-Guided Cardiac Ultrasonography Training
Ilan, O., Perlin, A., Peles, I., Shitrit, I. B., & Fuchs, L. (2025) · Academic Medicine · doi:10.1097/acm.0000000000006020 · Cited by 4
Self-citationLiu, Q., Ma, X., Li, S., Li, Z., Mo, Z., Lin, Y., Xie, H., & Ding, B. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07093-y · Cited by 4
Computer‐Based Competency Assessment in Point‐Of‐Care Ultrasound: A Systematic Review
Allan‐Blitz, L., Schwid, M., Duggan, N. M., Harari, R. E., Selame, L., Walsh, C. D., Papa, K., Chu, D. L., DIAS, R. D., & Goldsmith, A. J. (2025) · AEM Education and Training · doi:10.1002/aet2.70072 · Cited by 4
Bisignani, G., Volpe, L., Madeo, A., Riccardo, V., Bencardino, D., & Bonis, S. D. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14134708 · Cited by 5
Dayan, R. R., Karni, O., Shitrit, I. B., Gaufberg, R., Ilan, K., & Fuchs, L. (2025) · Perspectives on Medical Education · doi:10.5334/pme.1613 · Cited by 5
Self-citationHartline, C., Hartvickson, M. A. J. S., Perdue, M. J., Sandoval, C., Walker, J. D., Soules, C. P. T. A., & Mitchell, C. O. L. C. A. (2025) · Military Medicine · doi:10.1093/milmed/usaf215 · Cited by 3
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Perspectivas da inteligência artificial na educação médica: Uma revisão narrativa
Meirelles, L. D. M. R., do Amaral, A. J. P., Gevisiez, E. C., Jardim, J. V. S., Fernandes, L. R. S., & da Fonseca, W. L. M. S. (2025) · Congresso Médico Acadêmico UniFOA. · doi:10.47385/cmedunifoa.2065.2025 · Cited by 1
Alessi, M. R., Fedatto, M. C., Segalla, M. C., Pavanelo, C. D. V., Barberato, R. F. A., Carlotto, G. D., & França, G. J. (2025) · Jornal Vascular Brasileiro · doi:10.1590/1677-5449.202401112
Alessi, M. R., Fedatto, M. C., Segalla, M. C., Pavanelo, C. D. V., Barberato, R. F. A., Carlotto, G. D., & França, G. J. (2025) · Jornal Vascular Brasileiro · doi:10.1590/1677-5449.202401111
Safdar, A., Iftikhar, H., Arshad, H. A., Anwar, M., Khan, A. Q., & Kanwal, S. (2025) · VAWKUM Transactions on Computer Sciences · doi:10.21015/vtcs.v13i2.2278
Applications for Artificial Intelligence in Ultrasound Education: A Scoping Review (Preprint)Preprint
Brawley, R. R., Kumar, S., Douridas, L., & Brattain, L. J. (2026) · doi:10.2196/preprints.91382
del Solar, C. I. Q., Márquez, G. B., Garcia, S. L. Q., Flores, G. V. G., Torres, A. M., & Capuñay, J. E. C. (2026) · Más Vita · doi:10.47606/acven/mv0323
AI-Driven Ultrasound: Automation, Image Interpretation and Decision Support
Sarkar, N., Stancliff, H., & Srinivasareddy, S. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1015391
Rincón, E. H. H., Bastidas, J. C. K., Acevedo, S. B., & Florez, N. C. R. (2026) · Journal of Investigative Medicine · doi:10.1177/10815589261460883
Educação Superior e Inteligência Artificial: Desafios, Ética e Inovação em Goiás
Fernandes, M. D. S., de Oliveira, E. S. A., dos Santos, L. A., Miguel, C. B., Neto, A. C. B., Netto, A. B., Ferreira, J. R., & Moreira, M. E. (2026) · doi:10.35685/edufimes.7773
Metadata
- Authors
- Aronovitz, N., Hazan, I., Jedwab, R., Ben Shitrit, I., Quinn, A., Wacht, O., & Fuchs, L.
- Affiliations
- Ben-Gurion University of the Negev (IL)
- Corresponding author
- Aronovitz, Noam
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2024-03-28
- Volume
- 19
- Issue
- 3
- Pages
- e0299461
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC10977790
- OpenAlex
- W4393253708
- Cited by
- 19
- References
- 18
- Citations by year
- 2026: 6 · 2025: 13
- FWCI
- 7.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ben-Gurion University of the Negev
- 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
- Social Sciences › Education › Innovations in Medical Education
- Keywords
- point-of-care ultrasound, echocardiography, medical student training, real-time feedback
- MeSH terms
- Artificial Intelligence, Echocardiography, Humans, Students, Medical, Ultrasonography, Point-of-Care Systems
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
