# Biomarker integration and biosensor technologies enabling AI-driven insights into biological aging

## TL;DR

This paper reviews how AI and biosensors can improve the understanding of biological aging through key biomarkers.

## Contribution

The paper introduces a framework integrating AI and biosensors for measuring and interpreting biomarkers of aging.

## Key findings

- AI methods like machine learning enhance the interpretation of biomarker data for biological aging.
- Biomarkers such as CRP, IGF-1, IL-6, and GDF-15 are crucial for assessing biological age.
- AI-driven tools can lead to personalized health monitoring and disease risk assessment.

## Abstract

As the global population continues to age, there is an increasing demand for ways to accurately quantify the biological processes underlying aging. Biological age, unlike chronological age, reflects an individual’s physiological state, offering a more accurate measure of health-span and age-related decline. This review focuses on four key biochemical markers - C-Reactive Protein (CRP), Insulin like Growth Factor-1 (IGF-1), Interleukin-6 (IL-6), and Growth Differentiation Factor-15 (GDF-15) – and explores how Artificial Intelligence (AI) and biosensor technologies enhance their measurement and interpretation. AI-driven methods including machine learning, deep learning, and generative models facilitate the interpretation of high dimensional datasets and support the development of widely accessible, data-informed tools for health monitoring and disease risk assessment. This paves the way for a future medical system, enabling more personalized and accessible care, offering deeper, data-driven insights into individual health trajectories, risk profiles, and treatment response. The review additionally highlights the key challenges and future directions for the implementation of AI-driven methods in precision aging frameworks.