ArticleMayo Clinic proceedings. Digital health2026
Artificial Intelligence-Enhanced Cardiac Point-of-Care Ultrasound: A Prospective Single-Arm Study.
Article in Mayo Clinic proceedings. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05455541 (Reliability of Artificial Intelligence), which is not on this map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Reliability of Artificial Intelligence (AI)-Augmented Point-of-care Cardiac Ultrasound (POCCUS) in the Hands of Internists
Who cites it
2 citing papers in PubMed.
- Handheld Ultrasound: Technical Review and International Expert Consensus Recommendations.Diagnostics (Basel, Switzerland) · 2026Review
- Point-of-care echocardiography screening for hypertrophic cardiomyopathy using automated deep-learning analysis.European heart journal. Digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To evaluate the clinical utility of combining artificial intelligence (AI) with handheld focused cardiac ultrasound (FoCUS) performed by noncardiologist physicians in clinical care settings. Patients and Methods: In this prospective, single-arm study conducted from July 1, 2022, through December 31, 2023 (ClinicalTrials.gov NCT05455541), 660 adult patients presenting to the emergency department or internal medicine wards were assessed with handheld ultrasound devices enhanced by AI algorithms. These algorithms provided automated analysis of ventricular function, valvular disease, pericardial effusion, and inferior vena cava size. Participating physicians received focused training and performed examinations either in response to clinical suspicion or as part of routine evaluation. The primary outcome was whether AI-guided FoCUS contributed to new diagnoses, treatment modifications, or additional procedures. Results: Artificial intelligence-enhanced FoCUS identified clinically relevant cardiac findings in 193 patients (29%), including newly recognized valvular abnormalities and reduced left ventricular function. In 49 patients (7%), medical therapy was adjusted based on findings, and 9 patients (1.4%) underwent interventional procedures. Diagnostic performance analyses showed high sensitivity for detecting reduced left ventricular function and valvular disease, with lower sensitivity for right-sided abnormalities. Conclusion: This study demonstrates that integrating AI-enhanced FoCUS into routine workflows can provide clinically relevant information that may influence diagnostic assessment and management by noncardiology practitioners in acute care settings.
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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.