Evidence map›Paper›PMID 40622521›Full record

ReviewCurrent atherosclerosis reports2025

Artificial Intelligence-Enabled Point-of-Care Echocardiography: Bringing Precision Imaging to the Bedside.

Sasha-Ann East, Yanting Wang, Naveena Yanamala, Kameswari Maganti, Partho P Sengupta

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current atherosclerosis reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Review
  6. Review
  7. Article
  8. Review
  9. AI-Enhanced POCUS in Emergency Care.Diagnostics (Basel, Switzerland) · 2026
    Review
  10. Article
  11. Article
  12. The global roadmap for cardiovascular imaging: bridging the diagnostic divide.European heart journal. Imaging methods and practice · 2026
    Review
  13. Article
  14. Review
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

5 authors.

Sasha-Ann EastDivision of Cardiovascular Diseases and Hypertension, Department of Medicine, Rutgers Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, 08901, USA.
Yanting WangDivision of Cardiovascular Diseases and Hypertension, Department of Medicine, Rutgers Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, 08901, USA.
Naveena YanamalaDivision of Cardiovascular Diseases and Hypertension, Department of Medicine, Rutgers Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, 08901, USA.
Kameswari MagantiDivision of Cardiovascular Diseases and Hypertension, Department of Medicine, Rutgers Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, 08901, USA.
Partho P SenguptaDivision of Cardiovascular Diseases and Hypertension, Department of Medicine, Rutgers Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, 08901, USA. partho.sengupta@rutgers.edu.

Funding

Cardiac Ultrasound Radiomics-Guided Deep Neural Networks for Acute Myocardial Infarction Precision PhenotypingR01HL173998 · NHLBI · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · PI Partho P Sengupta · 2025 to 2026
$1.2M
NHLBI NIH HHS R01 HL173998
6 · The paper itself

Abstract

purpose of reviewThe integration of artificial intelligence (AI) with point-of-care ultrasound (POCUS) is transforming cardiovascular diagnostics by enhancing image acquisition, interpretation, and workflow efficiency. These advancements hold promise in expanding access to cardiovascular imaging in resource-limited settings and enabling early disease detection through screening applications. This review explores the opportunities and challenges of AI-enabled POCUS as it reshapes the landscape of cardiovascular imaging. RECENT

findingsAI-enabled systems can reduce operator dependency, improve image quality, and support clinicians-both novice and experienced-in capturing diagnostically valuable images, ultimately promoting consistency across diverse clinical environments. However, widespread adoption faces significant challenges, including concerns around algorithm generalizability, bias, explainability, clinician trust, and data privacy. Addressing these issues through standardized development, ethical oversight, and clinician-AI collaboration will be critical to safe and effective implementation. Looking ahead, emerging innovations-such as autonomous scanning, real-time predictive analytics, tele-ultrasound, and patient-performed imaging-underscore the transformative potential of AI-enabled POCUS in reshaping cardiovascular care and advancing equitable healthcare delivery worldwide.

Indexed as

Artificial IntelligenceCardiovascular DiseasesEchocardiographyPoint-of-Care SystemsHumansArtificial intelligenceDeep learningGenerative AIPoint-of-care ultrasound

Identifiers

PMID40622521
PMCPMC12234643

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.