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Health·The Trace·Dual reading·Published 2026-09-01

AI for cardiovascular risk prediction and early diagnosis in preventive cardiology

Source article: [Artificial intelligence in cardiovascular prevention: a strategic opportunity for risk prediction and early diagnosis]

Abstract: Cardiovascular diseases remain the leading cause of mortality and morbidity worldwide, with substantial impact in Italy. Cardiovascular prevention is a strategic priority, yet a significant gap persists between evidence-based guideline recommendations and their actual implementation in clinical practice. Artificial intelligence (AI), through machine learning and deep learning models, is emerging as a potentially transformative technology to bridge this gap, enabling more precise, dynamic, and personalized cardio…

TRV-2026-0955Peer-reviewedPermanent record — cite & verify
Trace impact reading

Contested: both sides are scored from claims and sources, not community votes.

P 68The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.G 71The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.
[Artificial intelligence in cardiovascular prevention: a strategic opportunity for risk prediction and early diagnosis]

Shaher7 by Giardello. CC BY-SA 3.0 · https://creativecommons.org/licenses/by-sa/3.0

The quick read

This peer-reviewed review examines AI, including machine learning and deep learning, for cardiovascular prevention in Italy and globally, where cardiovascular diseases remain the leading cause of mortality and morbidity. It surveys evidence that AI can deliver more precise, dynamic and personalized risk stratification than traditional scores and support early detection of subclinical disease via AI-enabled electrocardiography and opportunistic imaging.

The potential clinical impact is a move from reactive cardiology to predictive, proactive and precision-based prevention, but the article stresses that routine use is not yet established. It matters because improved stratification could target interventions more effectively, yet uncertainty remains about prospective performance, generalizability, interpretability and the digital and regulatory infrastructure needed for safe deployment.

Main points
  • Review focuses on AI for risk stratification, early detection of subclinical disease, and targeting patients for preventive interventions.
  • Discusses emerging determinants such as digital biomarkers, genetic data, and wearable devices beyond conventional risk scores.
  • Highlights opportunistic imaging including chest radiography, chest and coronary computed tomography, and mammography for subclinical atherosclerosis detection.
  • Describes integration pathways via electronic health records, clinical decision support systems, and telemonitoring networks.
Gain

AI models improve cardiovascular prevention by providing more precise, dynamic and personalized risk stratification than traditional scores and by enabling early detection of subclinical atrial fibrillation, left ventricular dysfunction and coronary disease through AI-enabled ECG and opportunistic imaging.

Problem

Implementation of AI for cardiovascular prevention remains limited by insufficient prospective evidence and randomized trials, lack of validation in heterogeneous populations, limited model interpretability, and inadequate digital and regulatory infrastructures, leaving a gap between guideline recommendations and real‑

The rundown

The review details how AI-enabled ECG can flag subclinical atrial fibrillation, left ventricular dysfunction and coronary artery disease, and how opportunistic analysis of chest radiography, chest and coronary CT, and mammography can reveal subclinical atherosclerosis.

It frames AI as incorporating digital biomarkers, genetic data and wearable devices into dynamic risk models, and discusses embedding these tools into electronic health records, decision support and telemonitoring to shift cardiology from reactive to predictive and proactive care.

What this doesn’t fix

Translation to routine care is constrained by lack of prospective validation and infrastructure gaps, requiring further trials and interpretability improvements before clinical adoption.

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