ArticleNature reviews. Cardiology2019
Artificial intelligence to improve the diagnosis of cardiovascular diseases.
Article in Nature reviews. Cardiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- A deep learning framework for standardized interpretation of multiparameter cardiac ultrasound and disease classification.iScience · 2026Article
- Comprehensive Analysis of Cardiovascular Diseases: Symptoms, Diagnosis, and AI Innovations.Bioengineering (Basel, Switzerland) · 2024Review
- Fetal membrane imaging: current and future perspectives-a review.Frontiers in physiology · 2024Review
- Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease.BMC cardiovascular disorders · 2023Article
- Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries.Journal of multidisciplinary healthcare · 2023Review
- Identification of hub genes and their correlation with immune infiltration in coronary artery disease through bioinformatics and machine learning methods.Journal of thoracic disease · 2022Article
- Advancing artificial intelligence-assisted pre-screening for fragile X syndrome.BMC medical informatics and decision making · 2022Article
- Article
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- Trends and Focus of Machine Learning Applications for Health Research.JAMA network open · 2019Article
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
30683888What 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.