eXplainable Artificial Intelligence (XAI) in aging clock models
This work addresses the need for interpretability in AI-driven health applications, specifically for aging research, but it appears to be an incremental review rather than presenting new results.
The paper discusses applying eXplainable Artificial Intelligence (XAI) to aging clock models, which are used in aging research to identify biomarkers and predict age-related diseases, but notes that XAI's potential in this area is not yet fully realized.
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.