Evidence map›Paper›PMID 38901544›Full record

ReviewThe Canadian journal of cardiology2024

Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges.

Gemina Doolub, Shaan Khurshid, Pascal Theriault-Lauzier, Alexis Nolin Lapalme, Olivier Tastet, Derek So, Elodie Labrecque Langlais, Denis Cobin, Robert Avram

Abstract readReview
In one paragraph

Review in The Canadian journal of cardiology, 2024. 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. Review
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  14. Artificial Intelligence in Medicine: A Specialty-Level Overview of Emerging AI Trends.JSLS : Journal of the Society of Laparoendoscopic Surgeons
    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

9 authors.

Gemina DoolubDepartment of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Québec, Canada.
Shaan KhurshidCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts, USA.
Pascal Theriault-LauzierDivision of Cardiovascular Medicine, Stanford School of Medicine, Palo Alto, California, USA.
Alexis Nolin LapalmeDepartment of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Québec, Canada; Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada; Mila-Québec AI Institute, Montréal, Québec, Canada.
Olivier TastetHeartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada.
Derek SoUniversity of Ottawa, Heart Institute, Ottawa, Ontario, Canada.
Elodie Labrecque LanglaisPolytechnique Montréal, Montréal, Québec, Canada.
Denis CobinHeartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada.
Robert AvramDepartment of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Québec, Canada; Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada. Electronic address: robert.avram.md@gmail.com.

Funding

Electrocardiogram-based deep learning and decision analysis to improve atrial fibrillation risk estimationK23HL169839 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Shaan Khurshid · 2023 to 2026
$860k
NHLBI NIH HHS K23 HL169839
6 · The paper itself

Abstract

This article reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidly and accurately process data for the prediction and diagnosis of acute cardiac conditions. The review examines AI's impact on patient health across various diagnostic tools such as echocardiography, electrocardiography, coronary angiography, cardiac computed tomography, and magnetic resonance imaging, discusses the regulatory landscape for AI in health care, and categorises AI algorithms by their risk levels. Furthermore, it addresses the challenges of data quality, generalisability, bias, transparency, and regulatory considerations, underscoring the necessity for inclusive data and robust validation processes. The review concludes with future perspectives on integrating AI into clinical workflows and the ongoing need for research, regulation, and innovation to harness AI's full potential in improving acute cardiac care.

Indexed as

Artificial IntelligenceAlgorithmsCardiovascular DiseasesHumans

Identifiers

PMID38901544
PMCPMC12131165

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.