ReviewGlobal cardiology science & practice2025
Artificial intelligence in coronary artery calcification scoring: Current progress and future directions.
Review in Global cardiology science & practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Exercise in Patients with Subclinical Atherosclerosis: Mechanisms, Clinical Evidence, and Practical Recommendations.Current atherosclerosis reports · 2026Review
- Reimagining cardiac care with AI, LLMs, blockchain, and metaverse.Global cardiology science & practice · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe primary purpose of this paper is to evaluate the role of artificial intelligence (AI) in enhancing coronary artery calcification (CAC) scoring for improved cardiovascular risk assessment.
methodsA narrative review was performed using data from PubMed, Scopus, and Semantic Scholar, focusing on publications from 2020 to 2025. The study includes research utilizing AI methodologies, including deep learning and machine learning, in CAC scoring. Key measurements included CAC scores from computed tomography (CT) images, inter-observer variability, and patient outcomes. Data analysis involved qualitative synthesis of findings and examination of performance metrics.
resultsAI algorithms significantly improved CAC score accuracy, with sensitivity and specificity rates of 90%. The use of AI reduced inter-observer variability by up to 30%, enabling more consistent risk assessments. Additionally, AI-enhanced CAC scoring effectively identified high-risk patients, leading to better-targeted preventive strategies compared to traditional methods.
conclusionThe incorporation of AI into CAC scoring holds promise for transforming cardiovascular risk assessment by enhancing accuracy and reliability. Future research should focus on validating AI tools across diverse populations, developing user-friendly clinical applications, and exploring AI's role in longitudinal cardiovascular health studies. Addressing these challenges will enhance the utility of CAC scoring and ultimately improve patient outcomes.
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.