Evidence map›Paper›PMID 42094314›Full record

ArticleMayo Clinic proceedings. Digital health2026

Artificial Intelligence-Enhanced Cardiac Point-of-Care Ultrasound: A Prospective Single-Arm Study.

Lior Fisher, Michael Fiman, Yuval Yarkoni, Ella Segal, Boris Fishman, Kobi Faierstein, Noa Rubin, Adiel Am-Shalom, Howard Amital, Qiong Zhao and 7 more

Registry-linked trialAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

NCT05455541 unknown statusnot on this map

Reliability of Artificial Intelligence (AI)-Augmented Point-of-care Cardiac Ultrasound (POCCUS) in the Hands of Internists

TypeobservationalSponsorSheba Medical CenterRan2022 to 2023Enrolled300ConditionsCardiovascular Diseases, Valvular Heart DiseaseArmsAI augmented POCUS examination
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Lior FisherLeviev Cardiovascular Institute, Sheba Medical Center, Tel-Aviv University, Tel-Aviv, Israel.
Michael FimanAISAP.ai, Ramat-Gan, Israel.
Yuval YarkoniLeviev Cardiovascular Institute, Sheba Medical Center, Tel-Aviv University, Tel-Aviv, Israel.
Ella SegalLeviev Cardiovascular Institute, Sheba Medical Center, Tel-Aviv University, Tel-Aviv, Israel.
Boris FishmanLeviev Cardiovascular Institute, Sheba Medical Center, Tel-Aviv University, Tel-Aviv, Israel.
Kobi FaiersteinLeviev Cardiovascular Institute, Sheba Medical Center, Tel-Aviv University, Tel-Aviv, Israel.
Noa RubinAISAP.ai, Ramat-Gan, Israel.
Adiel Am-ShalomAISAP.ai, Ramat-Gan, Israel.
Howard AmitalFaculty of Medicine, Sheba Medical Center, Tel-Aviv University, Tel-Aviv, Israel.
Qiong ZhaoInova Heart and Vascular Institute, Falls Church, VA.
Smadar KortStony Brook Heart Institute, Stony Brook, NY.
Praveen MehrotraThomas Jefferson University Hospital, Philadelphia, PA.
Ehud SchwammenthalLeviev Cardiovascular Institute, Sheba Medical Center, Tel-Aviv University, Tel-Aviv, Israel.
Robert KlempfnerLeviev Cardiovascular Institute, Sheba Medical Center, Tel-Aviv University, Tel-Aviv, Israel.
Eyal ZimlichmanLeviev Cardiovascular Institute, Sheba Medical Center, Tel-Aviv University, Tel-Aviv, Israel.
Ehud RaananiLeviev Cardiovascular Institute, Sheba Medical Center, Tel-Aviv University, Tel-Aviv, Israel.
Elad MaorLeviev Cardiovascular Institute, Sheba Medical Center, Tel-Aviv University, Tel-Aviv, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID42094314
PMCPMC13141710

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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.