Scoping review: AI-estimated biological age and cardiovascular risk
This scoping review of studies from 2019–2025 reports that AI-derived biological age, especially from ECGs, gave prognostic information beyond chronological age, but standardization, prospective validation and interpretability are still needed. [1]
Automatic summary · not editorially reviewed
The key findings
The review searched PubMed and Scopus for studies published between 2019 and 2025 on AI-estimated biological age and cardiovascular outcomes in humans. [1]
Deep learning models identified an age gap associated with increased risks of heart failure, atrial fibrillation and stroke. [1]
The authors state that standardization, prospective validation and improved interpretability are needed to support clinical implementation. [1]
The source at a glance
- Study type / format
- Scoping review of original research (abstract)
- Population / context
- Human participants in original studies on AI-estimated biological age and cardiovascular outcomes
- Summary based on
- Study abstract
- Original publication
- June 9, 2026
Review question on AI biological age and CVD
The authors note that cardiovascular diseases remain the leading cause of death worldwide and that risk rises with age. They argue biological age reflects physiological decline more accurately than chronological age and may improve cardiovascular risk prediction. Because AI now allows biological age to be estimated from electrocardiograms, imaging, biomarkers and omics data, they searched PubMed and Scopus for studies published between 2019 and 2025. Eligible were original studies applying AI or machine learning to estimate biological age or aging biomarkers in human participants and linking these estimates to cardiovascular outcomes.
Supporting source [1]
ECG age gap linked to heart failure, stroke risk
Findings were summarized narratively and grouped by methodological approach. AI-derived biological age, particularly ECG-predicted age, consistently provided prognostic information beyond chronological age. Deep learning models identified an age gap that was associated with increased risks of heart failure, atrial fibrillation and stroke. Retinal imaging-based biological age and biomarker-based machine learning models further supported the systemic nature of aging. Emerging approaches such as generative models offered insights into aging trajectories and lifestyle-linked aging phenotypes, and explainable AI highlighted ECG features contributing most to aging predictions.
Supporting source [1]
Abstract-only scope and implementation gaps
The review concludes that AI-based estimation of biological age is a promising biomarker for cardiovascular risk assessment. Although evidence across modalities is described as consistent, the authors state that further standardization, prospective validation and improved interpretability are needed before clinical implementation. This explainer is based on the abstract only; the full text was not reviewed, so details of the included studies, their designs, sample sizes and follow-up are not reported here. The described associations do not establish that AI-derived biological age causes cardiovascular events.
Supporting source [1]
How to interpret this
Abstract only; the full text was not reviewed. The abstract does not report the number of included studies, their designs, sample sizes or follow-up, and the described associations are not evidence of causation.
Based on the study abstract; the full paper, tables and supplementary data were not reviewed.
Source & supporting passages
Sudoso AM, Ciarpaglini L, Scuppa D, Trasatti E, Sciandrone M, Galiuto L. · europepmc.org · June 9, 2026