ReviewCurrent treatment options in cardiovascular medicine2019
Artificial Intelligence in Cardiovascular Medicine.
Review in Current treatment options in cardiovascular medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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
34 citing papers in PubMed.
- Synthetic artificial intelligence in cardiology: from generative models to clinical applications.European heart journal open · 2026Review
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- Synthetic generation of cardiac tissue motion from surface electrocardiograms.Nature cardiovascular research · 2025Article
- Bibliometric analysis of artificial intelligence applications in cardiovascular imaging: trends, impact, and emerging research areas.Annals of medicine and surgery (2012) · 2025Article
- Revolutionizing Cardiology: The Role of Artificial Intelligence in Echocardiography.Journal of clinical medicine · 2025Review
- Broadening Perspectives of Artificial Intelligence in Echocardiography.Cardiology and therapy · 2024Review
- Review
- Topological Data Analysis in Cardiovascular Signals: An Overview.Entropy (Basel, Switzerland) · 2024Review
- Automated MR Image Prescription of the Liver Using Deep Learning: Development, Evaluation, and Prospective Implementation.Journal of magnetic resonance imaging : JMRI · 2023Article
- Role of Deep Learning in Computed Tomography.Cureus · 2023Review
- Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus.PloS one · 2023Article
- Interplay between Artificial Intelligence and Biomechanics Modeling in the Cardiovascular Disease Prediction.Biomedicines · 2022Review
- Cephalometric Variables Prediction from Lateral Photographs Between Different Skeletal Patterns Using Regression Artificial Neural Networks.Turkish journal of orthodontics · 2022Article
- Classification of Electrocardiography Hybrid Convolutional Neural Network-Long Short Term Memory with Fully Connected Layer.Computational intelligence and neuroscience · 2022Article
- Artificial Intelligence Advancements in the Cardiovascular Imaging of Coronary Atherosclerosis.Frontiers in cardiovascular medicine · 2022Review
- Artificial intelligence in cardiology: Hope for the future and power for the present.Frontiers in cardiovascular medicine · 2022Review
- Emerging role of machine learning in cardiovascular disease investigation and translations.American heart journal plus : cardiology research and practice · 2021Article
- Artificial intelligence and machine learning in cardiovascular computed tomography.World journal of cardiology · 2021Review
- Applications of artificial intelligence/machine learning approaches in cardiovascular medicine: a systematic review with recommendations.European heart journal. Digital health · 2021Review
- Applications of artificial intelligence in cardiovascular imaging.Nature reviews. Cardiology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
purpose of reviewThe ripples of artificial intelligence are being felt in various sectors of human life. Machine learning, a subset of artificial intelligence, extracts information from large databases of information and is gaining traction in various fields of cardiology. In this review, we highlight noteworthy examples of machine learning utilization in echocardiography, nuclear cardiology, computed tomography, and magnetic resonance imaging over the past year. RECENT
findingsIn the past year, machine learning (ML) has expanded its boundaries in cardiology with several positive results. Some studies have integrated clinical and imaging information to further augment the accuracy of these ML algorithms. All the studies mentioned in this review have clearly demonstrated superior results of ML in relation to conventional approaches for identifying obstructions or predicting major adverse events in reference to conventional approaches. As the influx of data arriving from gradually evolving technologies in health care and wearable devices continues to be more complex, ML may serve as the bridge to transcend the gap between health care and patients in the future. In order to facilitate a seamless transition between both, a few issues must be resolved for a successful implementation of ML 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.