eXplainable Artificial Intelligence (XAI) in aging clock models
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 28 of its 152 references that are in the map. First posted on arXiv 21 Jul 2023.
Builds on
28 of 152 references.
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16 more
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.