ArticlePloS one2024
The effect of real-time EF automatic tool on cardiac ultrasound performance among medical students.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Effectiveness of Artificial Intelligence in Undergraduate Health Professions Education: Systematic Review and Meta-Analysis of Randomized Controlled Trials.JMIR medical education · 2026Pooled it
- Effectiveness of a multi-model teaching strategy to train emergency medicine residents to use point-of-care ultrasound (POCUS) for assessment of shock.BMC medical education · 2025Trial
- Development of a Deep Learning-Based Feedback Model to Assist Medical Students Learning Renal Ultrasound Acquisition: Mixed Methods Study.JMIR medical education · 2026Article
- AI-Enhanced POCUS in Emergency Care.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in undergraduate medical education: an updated scoping review.BMC medical education · 2025Article
- AI-Assisted LVEF Assessment Using a Handheld Ultrasound Device: A Single-Center Comparative Study Against Cardiac Magnetic Resonance Imaging.Journal of clinical medicine · 2025Article
- Computer-Based Competency Assessment in Point-Of-Care Ultrasound: A Systematic Review.AEM education and training · 2025Article
- Anatomic evaluation of the height of the carotid bifurcation by 4Jornal vascular brasileiro · 2025Article
- Principles for Developing a Large-Scale Point-of-Care Ultrasound Education Program: Insights from a Tertiary University Medical Center in Israel.Perspectives on medical education · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePoint-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.
methodsThe 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.
resultsThe 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.
conclusionsThe 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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