eXplainable Artificial Intelligence (XAI) in aging clock models

TL;DR

This paper reviews the application of explainable AI techniques to aging clock models, highlighting their importance in health-related predictions and analyzing existing literature across physiological systems.

Contribution

It provides a comprehensive overview of how XAI is applied in aging clock models and categorizes current research by physiological focus.

Findings

XAI enhances interpretability of aging models

Literature analysis across physiological systems

Identifies gaps in current XAI applications in aging

Abstract

eXplainable Artificial Intelligence (XAI) is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, e.g. in health care, when diagnosis, recommendations and treatment choices might rely on the decisions made by artificial intelligence systems. AI approaches have become widely used in aging research as well, in particular, in developing biological clock models and identifying biomarkers of aging and age-related diseases. However, the potential of XAI here awaits to be fully appreciated. We discuss the application of XAI for developing the "aging clocks" and present a comprehensive analysis of the literature categorized by the focus on particular physiological systems.