eXplainable Artificial Intelligence (XAI) in aging clock models

XAI is critical for healthcare AI applications: The paper emphasizes that explainability is "especially required in sensitive applications" like healthcare, where AI decisions directly impact diagnosis, treatment recommendations, and patient care—highlighting that transparency isn't optional but essential for responsible deployment.

Aging research lacks sufficient explainability focus: Despite AI becoming "widely used in aging research" for developing biological clock models and identifying disease biomarkers, the paper notes that "the potential of XAI here awaits to be fully appreciated," suggesting a significant gap between AI adoption and explainability practices in this field.

Black-box aging clocks need transparency: The paper addresses a specific problem where complex AI models predict biological age and aging-related diseases, but clinicians and patients cannot understand why these predictions are made—a transparency problem that could undermine trust and clinical adoption.

XAI enables better biomarker identification: By making aging clock models explainable, researchers can better understand which physiological systems and biomarkers actually drive aging predictions, potentially leading to more scientifically valid and actionable insights rather than opaque correlations.

Systematic XAI application across physiological systems is needed: The paper's comprehensive literature analysis organized by physiological systems suggests that responsible AI in aging research requires methodically applying explainability techniques across different biological domains, not treating it as an afterthought.