ReviewCirculation2024
Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.
Review in Circulation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02997254 (COMparison of Physiological Algorithms for Real-time Evaluation of Atrial Fibrillation), which is not on this map. Cited by 178 papers, 9 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.
COMparison of Physiological Algorithms for Real-time Evaluation of Atrial Fibrillation
Who cites it
178 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.Current oncology reports · 2026Pooled it
- Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Effectiveness of machine learning for diagnosis and prognostic prediction of congenital heart disease: a systematic review and meta-analysis.BMC pediatrics · 2026Pooled it
- Accuracy of the large language model ChatGPT in adult emergency department triage: a systematic review and meta-analysis.BMC emergency medicine · 2026Pooled it
- PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation: An Updated Checklist.JACC. Cardiovascular imaging · 2026Guideline
- Dried Blood Spots to Assess Cardiovascular-Kidney-Metabolic Health.Journal of the American Heart Association · 2025Pooled it
- Application of artificial intelligence in echocardiography from 2009 to 2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Use of artificial intelligence in predicting in-hospital cardiac and respiratory arrest in an acute care environment-implications for clinical practice.Frontiers in medical technology · 2025Pooled it
- Application of artificial intelligence in non-invasive cardiovascular imaging for coronary artery disease: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Artificial intelligence in clinical trials-state of the evidence, gaps, and next steps.EClinicalMedicine · 2026Review
- Artificial intelligence-enabled electrocardiography for detection of left ventricular diastolic dysfunction: a systematic review and meta-analysis.European heart journal. Digital health · 2026Review
- Clinical AI and Precision Medicine: Philosophical Questions About Labels, Disease, and Evidence.Journal of medical systems · 2026Article
- Article
- External Validation of Clinical Risk Scores and Machine Learning Models for Predicting 30-Day Cardiovascular Risk After Noncardiac Surgery: The PERICARE Study.Journal of clinical medicine · 2026Article
- Closed-Loop AI Systems for Arrhythmia Management: Toward Predictive, Personalized, and Safe Cardiac Care.Clinical cardiology · 2026Review
- A Review of the Role of Artificial Intelligence in Patients on Extracorporeal Membrane Oxygenation: A Scoping Review Study.Health science reports · 2026Review
- Review
- Prognostic value of AI-enabled quantitative coronary CT angiography for major adverse cardiovascular events: A systematic review and meta-analysis.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- Artificial intelligence in emergency medicine: from algorithmic performance to real-world clinical integration - understanding the implementation gap.International journal of emergency medicine · 2026Review
- Artificial intelligence and machine learning in global cardiac surgery: A scoping review.JTCVS open · 2026Article
118 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
15 authors.
Funding
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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.