Review of AI approaches to predicting biological age and their challenges
This review describes how artificial intelligence is used to predict biological age from complex biological data, including asynchronous aging of organs, and lists challenges such as data heterogeneity, limited generalizability and poor interpretability. [1]
Automatic summary · not editorially reviewed
The key findings
The review gives a systematic overview of AI-driven approaches to biological age prediction, covering biomarker selection, feature engineering, model development, bias correction and performance evaluation. [1]
The review discusses asynchronous aging, in which different organs or physiological systems age at distinct rates, and how deep learning and multimodal fusion may capture such system-specific patterns. [1]
The review states that challenges persist, including data heterogeneity, limited model generalizability, insufficient interpretability and barriers to clinical translation. [1]
The source at a glance
- Study type / format
- Review (abstract only)
- Population / context
- Not specified in the abstract; the review concerns biological age prediction in general.
- Summary based on
- Study abstract
- Original publication
- June 10, 2026
AI biological age prediction review scope
The source is a review that gives a systematic overview of AI-driven approaches to predicting biological age (BA), which is meant to assess health status and aging trajectories beyond chronological age (CA). It covers biomarker selection, feature engineering, model development, bias correction and performance evaluation. It also discusses asynchronous aging, where different organs or physiological systems age at distinct rates, and how deep learning and multimodal fusion may capture such system-specific patterns.
Supporting source [1]
Methods and applications described in the review
The review summarizes methods ranging from traditional machine learning algorithms to advanced neural architectures able to model nonlinear and heterogeneous aging processes. It also examines expanding applications of AI-based BA models in disease risk assessment, geriatric evaluation and population health monitoring. These are described as applications of the models, not as demonstrated benefits.
Supporting source [1]
Stated challenges and future research priorities
Despite rapid methodological progress, the review states that significant challenges persist: data heterogeneity, limited model generalizability, insufficient interpretability and barriers to clinical translation. It says addressing these will require standardized methodological practices, robust validation across diverse populations and interpretable, equitable AI systems. Future research should prioritize integrating multi-omics and longitudinal datasets with AI-driven frameworks to build reliable, system-level and clinically actionable BA prediction models that account for asynchronous aging.
Supporting source [1]
How to interpret this
This explainer is based only on the abstract; the full text was not reviewed. The abstract reports no study design details, sample sizes, follow-up or numerical results, so no performance figures can be given.
Based on the study abstract; the full paper, tables and supplementary data were not reviewed.
Source & supporting passages
Wang G, Ding P, Li Z, Tang Q, Tu L, Jiang T, Zhang L, Su B, Xu J, An H. · europepmc.org · June 10, 2026