Artificial Algorithms Outperform Traditional Models in Predicting Coronary Artery Disease.

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

3 citing papers in PubMed.

1. Predictive modeling with machine learning for low birth weight in the state of São Paulo: a retrospective population-based study.Sao Paulo medical journal = Revista paulista de medicina · 2026
   Article
2. Magnetoionography enhances diagnostic accuracy of magnetocardiography in coronary artery disease.Scientific reports · 2025
   Article
3. Incremental predictive value of liver fat fraction based on spectral detector CT for major adverse cardiovascular events in T2DM patients with suspected coronary artery disease.Cardiovascular diabetology · 2025
   Article

Corrections and comments

• Comment on
  Validation of an Artificial Intelligence Algorithm for Diagnostic Prediction of Coronary Disease: Comparison with a Traditional Statistical Model.2021

Authors and funding

3 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

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