ReviewCurrent cardiology reports2018
Machine Meets Biology: a Primer on Artificial Intelligence in Cardiology and Cardiac Imaging.
Review in Current cardiology reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 57 citations in OpenAlex.
- A narrative review on ethical considerations and challenges in AI-driven cardiology.Annals of medicine and surgery (2012) · 2025Review
- Subtyping Social Determinants of Health in the "All of Us" Program: Network Analysis and Visualization Study.Journal of medical Internet research · 2025Article
- RETRACTED: A novel spectral transformation technique based on special functions for improved chest X-ray image classification.PloS one · 2025Article
- Concepts and applications of digital twins in healthcare and medicine.Patterns (New York, N.Y.) · 2024Review
- Review
- Subtyping Social Determinants of Health inmedRxiv : the preprint server for health sciences · 2023Article
- A Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach.JMIR medical informatics · 2022Article
- Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation.NPJ digital medicine · 2022Article
- The health digital twin to tackle cardiovascular disease-a review of an emerging interdisciplinary field.NPJ digital medicine · 2022Review
- Public views on ethical issues in healthcare artificial intelligence: protocol for a scoping review.Systematic reviews · 2022Article
- Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis.Journal of medical Internet research · 2022Article
- Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography.American heart journal plus : cardiology research and practice · 2022Article
- Myocardial strain imaging in Duchenne muscular dystrophy.Frontiers in cardiovascular medicine · 2022Review
- Research Progress of Machine Learning and Deep Learning in Intelligent Diagnosis of the Coronary Atherosclerotic Heart Disease.Computational and mathematical methods in medicine · 2022Review
- Artificial intelligence in cardiology: Hope for the future and power for the present.Frontiers in cardiovascular medicine · 2022Review
- The health digital twin: advancing precision cardiovascular medicine.Nature reviews. Cardiology · 2021Article
- Application of Artificial Intelligence in Diagnosis of Craniopharyngioma.Frontiers in neurology · 2021Review
- Artificial Intelligence in Cardiology: Concepts, Tools and Challenges - "The Horse is the One Who Runs, You Must Be the Jockey".Arquivos brasileiros de cardiologia · 2020Article
- Artificial Intelligence, Machine Learning, and Cardiovascular Disease.Clinical Medicine Insights. Cardiology · 2020Review
- Classification of Background Parenchymal Uptake on Molecular Breast Imaging Using a Convolutional Neural Network.JCO clinical cancer informatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewAn understanding of the basics concepts of deep learning can be helpful in not only understanding the potential applications of this technique but also in critically reviewing literature in which neural networks are utilized for analysis and modeling. RECENT
findingsThe term "deep learning" has been applied to a subset of machine learning that utilizes a "neural network" and is often used interchangeably with "artificial intelligence." It has been increasingly utilized in healthcare for computational "learning", especially for pattern recognition for diagnostic imaging. Another promising application is the potential for these neural networks to improve the accuracy in the identification of patients who are at risk for cardiovascular events and could benefit most from preventive treatment in comparison with more conventional statistical techniques. The importance of such tailored cardiovascular risk assessment and disease management in individual patients is far reaching given that cardiovascular disease is the leading cause of morbidity and mortality in the world. Nearly half of myocardial infarctions and strokes occur in patients who are not predicted to be at risk for cardiovascular events by current guideline-based approaches. Equally important are individuals who are not at risk for cardiovascular events and yet are given expensive and unnecessary preventive treatment with potential untoward side effects. The application of powerful artificial intelligence/deep learning tools in medicine is likely to result in more effective and efficient health care delivery with the potential for significant cost savings by shifting preventative treatment from inappropriate to appropriate patient subgroups.
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.