ArticleArquivos brasileiros de cardiologia2020
Artificial Intelligence in Cardiology: Concepts, Tools and Challenges - "The Horse is the One Who Runs, You Must Be the Jockey".
Article in Arquivos brasileiros de cardiologia, 2020. 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 21 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
21 citing papers in PubMed.
- THERMOGRAPHY ACCURACY IN THE DIAGNOSIS OF CARPAL TUNNEL SYNDROME.Acta ortopedica brasileira · 2026Article
- Application of the U-Net Deep Learning Model for Segmenting Single-Photon Emission Computed Tomography Myocardial Perfusion Images.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning from latent spatiotemporal information of the heart: Identifying advanced bioimaging markers from echocardiograms.Biophysics reviews · 2024Review
- Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model: A cohort study.World journal of hepatology · 2024Article
- From beasts to bytes: Revolutionizing zoological research with artificial intelligence.Zoological research · 2023Review
- An online platform for COVID-19 diagnostic screening using a machine learning algorithm.Revista da Associacao Medica Brasileira (1992) · 2023Article
- Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography.American heart journal plus : cardiology research and practice · 2022Article
- Artificial intelligence and suicide prevention: a systematic review.European psychiatry : the journal of the Association of European Psychiatrists · 2022Review
- The Emergence of Artificial Intelligence in Cardiology: Current and Future Applications.Current cardiology reviews · 2022Review
- Initial application of deep learning to borescope detection of endoscope working channel damage and residue.Endoscopy international open · 2022Article
- Machine learning-based predictive modeling of depression in hypertensive populations.PloS one · 2022Article
- Artificial Algorithms Outperform Traditional Models in Predicting Coronary Artery Disease.Arquivos brasileiros de cardiologia · 2021Article
- Artificial intelligence in the diagnosis and management of arrhythmias.European heart journal · 2021Review
- The Challenges of Heart Failure Yesterday, Today and Tomorrow and the 20 Years of DEIC.Arquivos brasileiros de cardiologia · 2021Article
- Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging.Frontiers in cardiovascular medicine · 2021Article
- Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps.Frontiers in cardiovascular medicine · 2021Article
- What Can COVID-19 Teach Us about Using AI in Pandemics?Healthcare (Basel, Switzerland) · 2020Article
- Fully automated quantification of left ventricular volumes and function in cardiac MRI: clinical evaluation of a deep learning-based algorithm.The international journal of cardiovascular imaging · 2020Article
- Machine Learning in Electrocardiography and Echocardiography: Technological Advances in Clinical Cardiology.Current cardiology reports · 2020Review
- Ethics, Artificial Intelligence and Cardiology.Arquivos brasileiros de cardiologia · 2020Article
Corrections and comments
- Erratum issuedErrata.2020
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent advances at hardware level and the increasing requirement of personalization of care associated with the urgent needs of value creation for the patients has helped Artificial Intelligence (AI) to promote a significant paradigm shift in the most diverse areas of medical knowledge, particularly in Cardiology, for its ability to support decision-making and improve diagnostic and prognostic performance. In this context, the present work does a non-systematic review of the main papers published on AI in Cardiology, focusing on its main applications, potential impacts and challenges.
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.