eXplainable Artificial Intelligence (XAI) in aging clock models

Alena Kalyakulina, Igor Yusipov, Alexey Moskalev, Claudio Franceschi, Mikhail Ivanchenko
7/21/2023
cs.AIcs.LGeess.IV

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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.

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Cite this paper

@article{kalyakulina2023explainable,
  title  = {eXplainable Artificial Intelligence (XAI) in aging clock models},
  author = {Alena Kalyakulina and Igor Yusipov and Alexey Moskalev and Claudio Franceschi and Mikhail Ivanchenko},
  year   = {2023},
  eprint = {2307.13704},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.13704v3}
}

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