ArticleInternational journal of clinical practice2023
Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection Fraction.
Article in International journal of clinical practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Artificial intelligence for lung ultrasound interpretation: a systematic review.Frontiers in radiology · 2026Pooled it
- Artificial Intelligence in Cardiac Point-of-Care Ultrasound: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- 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 focused cardiac ultrasound in preventive cardiology - a perspective.NPJ cardiovascular health · 2025Review
- AI in Echocardiography: State-of-the-art Automated Measurement Techniques and Clinical Applications.JMA journal · 2025Review
- Roadmap to success: Blueprint for enterprise-wide deployment of a point-of-care ultrasound platform, inclusive of governance, policy, education, credentialing, and quality assurance (Part 2).Journal of clinical imaging science · 2025Article
- Critical care ultrasound: development, evolution, current and evolving clinical concepts in critical care medicine.Frontiers in medicine · 2025Review
- Revolutionizing Bladder Health: Artificial-Intelligence-Powered Automatic Measurement of Bladder Volume Using Two-Dimensional Ultrasound.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence and Echocardiography: From Literacy to Leadership, Redefining Roles, Responsibilities, and Sustainability in the Artificial Intelligence-driven Echocardiography Era.Journal of cardiovascular echographyReview
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.