ReviewEuropean heart journal. Digital health2021
Applications of artificial intelligence/machine learning approaches in cardiovascular medicine: a systematic review with recommendations.
Review in European heart journal. Digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 41 papers, 2 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
41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 citations in OpenAlex.
- In Silico Clinical Trials in Drug Development: A Systematic Review.Therapeutic innovation & regulatory science · 2026Pooled it
- Opportunities and Challenges of Cardiovascular Disease Risk Prediction for Primary Prevention Using Machine Learning and Electronic Health Records: A Systematic Review.Reviews in cardiovascular medicine · 2025Pooled it
- Impact of artificial intelligence on cardiovascular workflow, engagement, and outcomes: a systematic review.NPJ digital medicine · 2026Article
- Artificial Intelligence in Cardiovascular Disease Prevention: Current Applications and Future Perspectives.Anatolian journal of cardiology · 2026Review
- A feature selection-based oblique hyperplane for oblique random survival forests.BMC medical research methodology · 2026Article
- Comparative performance of four large language models in generating evidence-based exercise prescriptions using FITT-VP framework.Frontiers in physiology · 2026Article
- Artificial intelligence in cardiology: implications for healthcare outcomes.Frontiers in artificial intelligence · 2026Review
- Artificial Intelligence-Enabled Electrocardiogram for the Detection and Management of Cancer Therapy-Related Cardiotoxicity.Cancer innovation · 2025Article
- Review
- Characteristics of cohort data management systems (CDMS): a scoping review.BMC medical research methodology · 2025Article
- Optimized feature selection and advanced machine learning for stroke risk prediction in revascularized coronary artery disease patients.BMC medical informatics and decision making · 2025Article
- The Impact of Artificial Intelligence on Women's Cardiovascular Disease Care.Current cardiology reports · 2025Review
- State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- Applications, challenges and future directions of artificial intelligence in cardio-oncology.European journal of clinical investigation · 2025Review
- Use of Artificial Intelligence Applied to Electrocardiogram for Diagnosis of Left Ventricular Systolic Dysfunction.Arquivos brasileiros de cardiologia · 2025Article
- Omics-based Approach Towards Macrophages: New Perspectives of Biology and Function in the Normal and Diseased Heart.International journal of biological sciences · 2025Review
- The Role of Artificial Intelligence in Cardiovascular Disease Risk Prediction: An Updated Review on Current Understanding and Future Research.Current cardiology reviews · 2025Review
- Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.European heart journal. Digital health · 2025Review
- Narrative review: updates and strategies for reducing door-to-balloon time in ST-elevation myocardial infarction care.Frontiers in cardiovascular medicine · 2025Review
- Artificial Intelligence Algorithms in Cardiovascular Medicine: An Attainable Promise to Improve Patient Outcomes or an Inaccessible Investment?Current cardiology reports · 2024Review
Corrections and comments
- Erratum issuedErratum.2022
Authors and funding
16 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Artificial intelligence (AI) and machine learning (ML) promise vast advances in medicine. The current state of AI/ML applications in cardiovascular medicine is largely unknown. This systematic review aims to close this gap and provides recommendations for future applications. Methods and results: Pubmed and EMBASE were searched for applied publications using AI/ML approaches in cardiovascular medicine without limitations regarding study design or study population. The PRISMA statement was followed in this review. A total of 215 studies were identified and included in the final analysis. The majority (87%) of methods applied belong to the context of supervised learning. Within this group, tree-based methods were most commonly used, followed by network and regression analyses as well as boosting approaches. Concerning the areas of application, the most common disease context was coronary artery disease followed by heart failure and heart rhythm disorders. Often, different input types such as electronic health records and images were combined in one AI/ML application. Only a minority of publications investigated reproducibility and generalizability or provided a clinical trial registration. Conclusions: A major finding is that methodology may overlap even with similar data. Since we observed marked variation in quality, reporting of the evaluation and transparency of data and methods urgently need to be improved.
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.