ReviewJournal of the American College of Cardiology2021
Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review.
Review in Journal of the American College of Cardiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 168 papers, 4 of them syntheses 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
168 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Comparison of machine learning models with conventional statistical methods for prediction of percutaneous coronary intervention outcomes: a systematic review and meta-analysis.BMC cardiovascular disorders · 2025Pooled it
- Artificial Intelligence in the Heart of Medicine: A Systematic Approach to Transforming Arrhythmia Care with Intelligent Systems.Current cardiology reviews · 2025Pooled it
- BioBERT-powered synergy: advanced bibliometric and molecular insights into prostate cancer bone metastasis.Frontiers in immunology · 2025Pooled it
- Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis.European heart journal. Quality of care & clinical outcomes · 2023Pooled it
- Machine learning-enhanced echocardiography for screening coronary artery disease.Biomedical engineering online · 2023Trial
- Impact of preprocedural biological markers on 10-year mortality in the SYNTAXES trial.EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology · 2022Trial
- A Survey on Perspectives Toward Artificial Intelligence Among Italian Interventional Cardiologists.Journal of clinical medicine · 2026Article
- Health Electronic Assessment of Risks and Trends using Biometric Equipment and Technology (HEARTBEAT): study design and rationale.European heart journal. Digital health · 2026Article
- Machine learning and traditional regression approaches to predict risk of cardiovascular events in patients with acute coronary syndrome.BMC medical informatics and decision making · 2026Observational
- Review
- Development and validation of a multimodal machine learning prediction model for heart failure after acute myocardial infarction.Clinical and experimental medicine · 2026Article
- Prediction of Risk of Unplanned Readmission or Death in Elderly Heart Failure Patients During the Vulnerable Phase: Machine Learning With SHAP Interpretation.FASEB bioAdvances · 2026Article
- Innovative Advances in Non-Invasive Detection Technologies for Heart Failure: Synergistic Application of Multimodal Sensing and Intelligent Algorithms.Reviews in cardiovascular medicine · 2026Review
- Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling.Advanced healthcare materials · 2026Review
- The portability paradox of foundation models for clinical decision support.NPJ digital medicine · 2026Article
- Left atrial flow and thrombosis risk from 4D CT contrast dynamics by physics-informed neural network and indicator dilution theory.bioRxiv : the preprint server for biology · 2026Article
- Article
- Clinical risk score for cardiac death or heart failure hospitalization in moderate aortic stenosis.Echo research and practice · 2026Article
- Artificial Intelligence in Cardiovascular Imaging: From Automated Acquisition to Precision Diagnostics and Clinical Decision Support.Medical sciences (Basel, Switzerland) · 2026Review
- Artificial Intelligence and the Expanding Universe of Cardio-Oncology: Beyond Detection Toward Prediction and Prevention of Therapy-Related Cardiotoxicity-A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2026Review
108 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The role of physicians has always been to synthesize the data available to them to identify diagnostic patterns that guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clinical experts in some of these tasks. Machine learning has the potential to benefit patients and cardiologists, but only if clinicians take an active role in bringing these new algorithms into practice. The aim of this review is to introduce clinicians who are not data science experts to key concepts in machine learning that will allow them to better understand the field and evaluate new literature and developments. The current published data in machine learning for cardiovascular disease is then summarized, using both a bibliometric survey, with code publicly available to enable similar analysis for any research topic of interest, and select case studies. Finally, several ways that clinicians can and must be involved in this emerging field are presented.
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.