Related Experiment Video
Updated: Jun 27, 2026
08:53
High-Throughput Behavioral Aging and Lifespan Assays Using the Lifespan Machine
Published on: January 26, 2024
Artificial intelligence approaches in biological age prediction: current status and challenges
Guangjun Wang1, Pengcheng Ding1,2, Zihui Li1
1Digital and Intelligent Health Research Center, Anqing Normal University, Anqing, China.
British Journal of Biomedical Science
|June 26, 2026
Summary
Artificial intelligence (AI) advances biological age (BA) prediction beyond chronological age (CA). AI, especially deep learning, captures complex aging patterns and asynchronous aging, but challenges in data and translation remain.
Area of Science:
- Biomedical Informatics
- Gerontology
- Artificial Intelligence
Background:
- Biological age (BA) prediction is crucial for assessing health and aging trajectories.
- Chronological age (CA) does not fully capture individual aging rates.
- Artificial intelligence (AI) enables analysis of complex biological data for BA prediction.
Purpose of the Study:
- To provide a systematic overview of AI-driven approaches for biological age prediction.
- To highlight AI's role in understanding asynchronous aging.
- To examine the applications and challenges of AI in BA prediction.
Main Methods:
- Review of AI methodologies for BA prediction, including machine learning and deep learning.
- Analysis of biomarker selection, feature engineering, and model development.
- Examination of bias correction, performance evaluation, and multimodal fusion techniques.
Main Results:
- AI, particularly deep learning, effectively models nonlinear and heterogeneous aging processes.
- AI tools capture system-specific aging patterns indicative of asynchronous aging.
- AI-based BA models show promise in disease risk assessment and geriatric evaluation.
Conclusions:
- AI significantly accelerates biological age prediction, offering insights into health status.
- Addressing challenges like data heterogeneity and clinical translation is vital for AI in aging research.
- Future research should focus on multi-omics and longitudinal data integration for robust, equitable BA models.
Related Concept Videos
01:26Aging
Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
Cellular Clock Theory
The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...
Cellular Clock Theory
The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...
01:16Non-equilibrium in the Cell
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
01:30Biological Influences on Intelligence
Intelligence is often thought to be linked to brain size, but the relationship is more complex than that. While brain size does correlate modestly with some abilities, like verbal skills, the connection is weaker for others, such as spatial reasoning. Other factors, like brain structure, also play crucial roles. For instance, despite Einstein's smaller-than-average brain, his parietal cortex, which is involved in spatial reasoning, was 15% wider, suggesting that neural density might matter more...