Task-shifting to nonexperts using artificial intelligence-guided point-of-care ultrasound: a cohort study of patient selection, image quality, and learning curves.

Leah Wright, Cheng Hwee Soh, Bastian Seidel, Angus Baumann, Tony Mylius, Christopher Yu, Sudhir Wahi, Thomas H Marwick

Abstract read

In one paragraph

Article in European heart journal. Imaging methods and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Does the reported learning curve of artificial intelligence-guided point-of-care ultrasound reflect operator competency or study design?European heart journal. Imaging methods and practice · 2026
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

8 authors.

Leah WrightImaging Research Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia.ORCID https://orcid.org/0000-0003-3269-6287

Cheng Hwee SohImaging Research Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia.

Bastian SeidelOchre Medical Centre, Huonville, Tasmania, Australia.

Angus BaumannAlice Springs Hospital, The Gap, Northern Territory, Australia.

Tony MyliusWestern Australian Country Health Service, Merredin District Hospital, Wheatbelt, Western Australia, Australia.

Christopher YuWalgett Aboriginal Medical Service Limited, Walgett, New South Wales, Australia.

Sudhir WahiPrincess Alexandra Hospital, University of Queensland, Woolloongabba, Queensland, Australia.

Thomas H MarwickImaging Research Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia.ORCID https://orcid.org/0000-0001-9065-0899

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: To define rates of diagnostic image acquisition, clinical drivers of image quality and the learning curve for artificial intelligence (AI)-guided image acquisition on point-of-care ultrasound (AI-POCUS) in rural and remote communities. Methods and results: AI-guided image acquisition on point-of-care ultrasound was performed using AI software integrated with a desktop ultrasound system in 181 participants (65 ± 15 years, 47% female). A standardized training protocol included online material, lab attendance for 1 day, and online mentoring. Diagnostic-quality images were obtained from 72% of parasternal and 55% of apical images ( Conclusion: In rural community practice, the learning curve associated with AI-POCUS diagnostic quality seems longer than reported in other studies from inpatient settings. In novice users, diagnostic quality is greater in the parasternal than the apical windows.

Indexed as

Artificial intelligenceEchocardiographyHand-held ultrasoundNonexpert acquisitionRural and remote

Identifiers

PMID42441084

DOI10.1093/ehjimp/qyag101

PMCPMC13335798

What OpenQuestion holds

Textmetadata

LicenceCC BY

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Registered trials

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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.