ReviewCureus2025
Non-invasive Assessment of Coronary Artery Disease: The Role of AI in the Current Status and Future Directions.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
- Association between platelet-to-lymphocyte ratio and 28-day all-cause mortality in patients with asymptomatic coronary artery disease: a cohort study.European journal of medical research · 2025Article
- A Plasma Extracellular Vesicle-Derived microRNA Signature as a Potential Biomarker for Subclinical Coronary Atherosclerosis.International journal of molecular sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronary artery disease (CAD) remains a significant public health concern due to its high morbidity and mortality rates. Early detection and timely evaluation are crucial for improving patient outcomes. While both invasive and non-invasive methods are available for assessing CAD risk, non-invasive approaches minimize the complications associated with invasive procedures. Over the past two decades, advancements in artificial intelligence (AI), particularly machine learning techniques such as deep learning and natural language processing, have revolutionized cardiology. These technologies enhance diagnostic accuracy and clinical efficiency in non-invasive CAD evaluation. However, the broader adoption of AI faces critical challenges, including ethical concerns such as data privacy, high computational costs, and resource allocation disparities. This article explores the current landscape of non-invasive CAD assessment, highlighting the transformative potential and associated challenges of AI integration.
Indexed as
Identifiers
What Socratic 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 Socratic graph.