Evidence map›Paper›PMID 37230652›Full record

ReviewHeart failure clinics2023

The Emerging Role of Artificial Intelligence in Valvular Heart Disease.

Caroline Canning, James Guo, Akhil Narang, James D Thomas, Faraz S Ahmad

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Heart failure clinics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. 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.

Caroline CanningDivision of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 North St. Clair Street, Suite 600, Chicago, IL 60611, USA; Bluhm Cardiovascular Institute Center for Artificial Intelligence, Northwestern Medicine, Chicago, IL, USA. Electronic address: https://twitter.com/carolinecanning.
James GuoDivision of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 North St. Clair Street, Suite 600, Chicago, IL 60611, USA; Bluhm Cardiovascular Institute Center for Artificial Intelligence, Northwestern Medicine, Chicago, IL, USA.
Akhil NarangDivision of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 North St. Clair Street, Suite 600, Chicago, IL 60611, USA; Bluhm Cardiovascular Institute Center for Artificial Intelligence, Northwestern Medicine, Chicago, IL, USA. Electronic address: https://twitter.com/AkhilNarangMD.
James D ThomasDivision of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 North St. Clair Street, Suite 600, Chicago, IL 60611, USA; Bluhm Cardiovascular Institute Center for Artificial Intelligence, Northwestern Medicine, Chicago, IL, USA. Electronic address: https://twitter.com/jamesdthomasMD1.
Faraz S AhmadDivision of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 North St. Clair Street, Suite 600, Chicago, IL 60611, USA; Bluhm Cardiovascular Institute Center for Artificial Intelligence, Northwestern Medicine, Chicago, IL, USA; Division of Health and Biomedical informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. Electronic address: faraz.ahmad@northwestern.edu.

Funding

Electronic Health Record Nudges to Improve Quality of Care in Heart FailureK23HL155970 · NHLBI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI AHMAD, FARAZ SHAFIQUE · 2021 to 2024
$750k
NHLBI NIH HHS K23 HL155970
6 · The paper itself

Abstract

Valvular heart disease (VHD) is a morbid condition in which timely identification and evidence-based treatments can lead to improved outcomes. Artificial intelligence broadly refers to the ability for computers to perform tasks and problem solve like the human mind. Studies applying AI to VHD have used a variety of structured (eg, sociodemographic, clinical) and unstructured (eg, electrocardiogram, phonocardiogram, and echocardiograms) and machine learning modeling approaches. Additional researches in diverse populations, including prospective clinical trials, are needed to evaluate the effectiveness and value of AI-enabled medical technologies in clinical care for patients with VHD.

Indexed as

Artificial IntelligenceHeart Valve DiseasesHumansMachine LearningProspective StudiesArtificial intelligenceDeep learningHeart failureMachine learningNatural language processingValvular heart disease

Identifiers

PMID37230652
PMCPMC11267973

What OpenQuestion holds

Textmetadata
LicenceTDM
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 OpenQuestion graph.