eXplainable Artificial Intelligence (XAI) in aging clock models

Alena Kalyakulina; Igor Yusipov; Alexey Moskalev; Claudio Franceschi; Mikhail Ivanchenko

doi:10.1016/j.arr.2023.102144

eXplainable Artificial Intelligence (XAI) in aging clock models

Ageing Res Rev. 2024 Jan:93:102144. doi: 10.1016/j.arr.2023.102144. Epub 2023 Nov 28.

Authors

Alena Kalyakulina¹, Igor Yusipov², Alexey Moskalev³, Claudio Franceschi³, Mikhail Ivanchenko⁴

Affiliations

¹ Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Research Center for Trusted Artificial Intelligence, The Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow 109004, Russia; Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia. Electronic address: kalyakulina.alena@gmail.com.
² Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Research Center for Trusted Artificial Intelligence, The Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow 109004, Russia; Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia.
³ Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia.
⁴ Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia.

PMID: 38030090
DOI: 10.1016/j.arr.2023.102144

Abstract

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.

Keywords: Age-related diseases; Aging; Explainable artificial intelligence; Longevity; Machine learning.

Publication types

Review

MeSH terms

Aging
Artificial Intelligence*
Humans
Machine Learning*