ReviewCardiology and therapy2022
Applications of Machine Learning in Cardiology.
Review in Cardiology and therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review and comparison of machine learning and conventional statistical models for predicting cardiovascular events in dialysis patients.Renal failure · 2025Pooled it
- Review
- Multi-Omics and Artificial Intelligence in Cardiovascular Medicine: From Mechanistic Insights to Clinical Translation.Biomedicines · 2026Review
- Characterizing industry payments for FDA-approved AI medical devices.Health affairs scholar · 2025Article
- Review
- Harnessing Artificial Intelligence in Interventional Cardiology: A Systematic Review of Current Applications.Cureus · 2025Review
- Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review.BMJ open · 2025Article
- Artificial Intelligence in Cardiology: General Perspectives and Focus on Interventional Cardiology.Anatolian journal of cardiology · 2025Review
- A Machine Learning-Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study.Journal of medical Internet research · 2024Article
- Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?Current atherosclerosis reports · 2024Review
- Broadening Perspectives of Artificial Intelligence in Echocardiography.Cardiology and therapy · 2024Review
- Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review protocol.BMJ open · 2024Article
- Can Artificial Intelligence Improve the Readability of Patient Education Materials on Aortic Stenosis? A Pilot Study.Cardiology and therapy · 2024Article
- Article
- Cardiac tissue engineering: an emerging approach to the treatment of heart failure.Frontiers in bioengineering and biotechnology · 2024Review
- Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review.Applied bionics and biomechanics · 2024Article
- Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging.PloS one · 2023Article
- Prediction of acute kidney injury in intensive care unit patients based on interpretable machine learning.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.
Indexed as
Identifiers
What OpenQuestion holds
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.