ReviewClinical Medicine Insights. Cardiology2020
Artificial Intelligence, Machine Learning, and Cardiovascular Disease.
Review in Clinical Medicine Insights. Cardiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 5 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
69 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Applications of artificial intelligence in chronic kidney disease: a systematic review.Revista de saude publica · 2026Pooled it
- Number of Publications on New Clinical Prediction Models: A Bibliometric Review.JMIR medical informatics · 2025Pooled it
- Artificial Intelligence in the Heart of Medicine: A Systematic Approach to Transforming Arrhythmia Care with Intelligent Systems.Current cardiology reviews · 2025Pooled it
- Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023.Frontiers in physiology · 2023Pooled it
- Systematic review of current natural language processing methods and applications in cardiology.Heart (British Cardiac Society) · 2022Pooled it
- Multi-domain identification of myocardial infarction incidence using explainable AI: the overlooked role of periodontal health.Scientific reports · 2026Article
- A multi-view autoencoder architecture with self-adaptive feature recalibration and confidence-aware ensemble for heart disease classification.Scientific reports · 2026Article
- Left ventricular assist devices and right heart dysfunction: a review of impact mechanisms, evaluation methods, and prevention and management.Journal of cardiothoracic surgery · 2026Review
- Comparative evaluation of deep learning models for cardiovascular disease diagnosis and classification.Scientific reports · 2026Article
- Artificial Intelligence in Coronary Plaque Characterization: Clinical Implications, Evidence Gaps, and Future Directions.Journal of clinical medicine · 2026Review
- Development and implementation of explainable AI-based machine learning models for predicting hospital stay and treatment costs in cardiovascular patients.Scientific reports · 2025Article
- Beyond volumes and ejection fraction: practical insights into cine-CMR interpretation and applications.The international journal of cardiovascular imaging · 2025Review
- Machine learning-based prediction model for post-stroke cerebral-cardiac syndrome: a risk stratification study.Scientific reports · 2025Article
- Review
- Advancements in Sensor Technology for Monitoring and Management of Chronic Coronary Syndrome.Sensors (Basel, Switzerland) · 2025Review
- Key factors in predictive analysis of cardiovascular risks in public health.Scientific reports · 2025Article
- Application of machine learning for early detection of chronic diseases in Africa.Journal of public health research · 2025Article
- Machine learning-assisted point-of-care diagnostics for cardiovascular healthcare.Bioengineering & translational medicine · 2025Review
- Review
- Volatilome and machine learning in ischemic heart disease: Current challenges and future perspectives.World journal of cardiology · 2025Article
9 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
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI)-based applications have found widespread applications in many fields of science, technology, and medicine. The use of enhanced computing power of machines in clinical medicine and diagnostics has been under exploration since the 1960s. More recently, with the advent of advances in computing, algorithms enabling machine learning, especially deep learning networks that mimic the human brain in function, there has been renewed interest to use them in clinical medicine. In cardiovascular medicine, AI-based systems have found new applications in cardiovascular imaging, cardiovascular risk prediction, and newer drug targets. This article aims to describe different AI applications including machine learning and deep learning and their applications in cardiovascular medicine. AI-based applications have enhanced our understanding of different phenotypes of heart failure and congenital heart disease. These applications have led to newer treatment strategies for different types of cardiovascular diseases, newer approach to cardiovascular drug therapy and postmarketing survey of prescription drugs. However, there are several challenges in the clinical use of AI-based applications and interpretation of the results including data privacy, poorly selected/outdated data, selection bias, and unintentional continuance of historical biases/stereotypes in the data which can lead to erroneous conclusions. Still, AI is a transformative technology and has immense potential in health care.
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.