Evidence map›Paper›PMID 42571809›Full record

ArticleContemporary clinical trials2026

AI echo INSIGHT study: A prospective blinded randomized trial of artificial intelligence echocardiogram interpretation.

Keane K Lee, Niranjan Kumar, Sahir Eusuff, Milos Vukadinovic, Hirotaka Ieki, Mason J Moore, Andrew P Ambrosy, Alan S Go, James Jang, Howard Dinh and 5 more

Registry-linked trialAbstract readClinical Trial Protocol
PubMed Full text at PMC Publisher
In one paragraph

Article in Contemporary clinical trials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07229300 (Artificial Intelligence Echocardiogram Interpretation for Systematic Integration and Guided Healthcare Transformation), which is not on this map. Not yet cited in PubMed.

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

NCT07229300 naenrolling by invitationnot on this map

Artificial Intelligence Echocardiogram Interpretation for Systematic Integration and Guided Healthcare Transformation (The AI ECHO INSIGHT Study)

TypeinterventionalSponsorKaiser PermanenteRan2025 to 2026Enrolled1,200ConditionsEchocardiography, Transthoracic, Echocardiographic SoftwareArmsAI-Assisted Echocardiogram Interpretation, Sonographer-Assisted Echocardiogram Interpretation, Cardiologist-Only Echocardiogram Interpretation
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Keane K LeeDivision of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA; Kaiser Permanente Santa Clara Homestead Medical Center, Santa Clara, CA, USA. Electronic address: Keane.K.Lee@kp.org.
Niranjan KumarDivision of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA; Kaiser Permanente Santa Clara Homestead Medical Center, Santa Clara, CA, USA.
Sahir EusuffDivision of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA.
Milos VukadinovicUniversity of California Los Angeles, Los Angeles, CA, USA.
Hirotaka IekiStanford University, Stanford, CA, USA.
Mason J MooreDivision of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA.
Andrew P AmbrosyDivision of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA.
Alan S GoDivision of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA; Department of Health System Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, USA; Departments of Epidemiology, Biostatistics and Medicine, University of California, San Francisco, San Francisco, CA, Usa.
James JangThe Permanente Medical Group, Kaiser Permanente Northern California, USA.
Howard DinhThe Permanente Medical Group, Kaiser Permanente Northern California, USA.
Ivy A KuDepartment of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, CA, USA; The Permanente Medical Group, Kaiser Permanente Northern California, USA.
Vincent LiuDivision of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA.
David OuyangDivision of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA; Kaiser Permanente Santa Clara Homestead Medical Center, Santa Clara, CA, USA.
Brian ClaggettDivision of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Bryan HeStanford University, Stanford, CA, USA.

Funding

Deep Learning Based Phenotyping and Outcomes Prediction for Valvular Heart DiseaseR01HL173526 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI David Ouyang · 2024 to 2026
$2.5M
Artificial Intelligence Imaging Biomarkers of Longitudinal Cardiovascular StressR01HL173487 · NHLBI · KAISER FOUNDATION RESEARCH INSTITUTE · PI David Ouyang · 2025 to 2026
$1.4M
Deep Learning Assessment of the Right Ventricle: Function, Etiology, and PrognosisR00HL157421 · NHLBI · KAISER FOUNDATION RESEARCH INSTITUTE · PI OUYANG, DAVID · 2023 to 2025
$747k
NHLBI NIH HHS R00 HL157421NHLBI NIH HHS R01 HL173487NHLBI NIH HHS R01 HL173526
6 · The paper itself

Abstract

backgroundTransthoracic echocardiography (TTE) is the most commonly performed cardiac imaging modality with over 30 million studies annually. Demand for timely expert interpretation continues to outpace capacity, creating diagnostic delays and inter-observer variability that impact patient care. Recent research has suggested computer vision artificial intelligence (AI) models can generate accurate preliminary comprehensive TTE reports, however, prospective evaluation is needed to determine whether AI-assisted TTE interpretation can improve clinician efficiency while preserving diagnostic accuracy.

methodsAI ECHO INSIGHT is a prospective randomized blinded clinical trial conducted at Kaiser Permanente Northern California that will evaluate 1200 historical TTE studies (1000 consecutive unselected studies plus 200 with moderate or greater valvular disease) interpreted using three workflows: (1) AI-generated preliminary report finalized by a blinded cardiologist (AI-assisted); (2) cardiologist-generated preliminary report finalized by a blinded cardiologist (cardiologist-assisted); and (3) sonographer-generated preliminary report finalized by a blinded cardiologist (sonographer-assisted). The primary outcome is the rate of substantial change between preliminary and final reports, comparing the AI-assisted workflow to the pooled cardiologist-assisted and sonographer-assisted workflows. Secondary outcomes include cardiologist interpretation time for report finalization, superiority testing for diagnostic accuracy, and reporting consistency.

conclusionAI ECHO INSIGHT is a prospective randomized blinded clinical trial evaluating the clinical impact of AI-assisted TTE interpretation on diagnostic accuracy, cardiologist efficiency, and reporting consistency in real-world echocardiography workflows.

trial registrationClinicalTrials.gov registration number NCT07229300.

Indexed as

Artificial IntelligenceEchocardiographyImage Interpretation, Computer-AssistedHumansProspective StudiesRandomized Controlled Trials as TopicArtificial intelligenceClinical workflowComputer visionDeep learningEchocardiography

Identifiers

PMID42571809
PMCPMC13693553

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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.