Algorithms to Ageless: AI in Anti-Aging Medicine

Abstract

The adoption of artificial intelligence (AI) in medicine in general and in scientific anti-aging efforts in particular represents a new era of research, diagnostics, and therapy. We here review and discuss the AI-driven methodologies that accelerate the identification of therapeutic targets, predictive biomarkers, and precision-driven interventions, as well as recent breakthroughs, including the application of deep learning (DL) algorithms to screen large chemical libraries, leading to the discovery of senolytic compounds that selectively eliminate aging cells. AI-powered biological aging clocks, trained on genomic, proteomic, metabolomic, and epigenomic data, enable accurate predictions of biological age that may help to optimize early intervention strategies for age-related diseases. AI-based bioinformatics platforms have identified novel compounds that enhance collagen synthesis and mitigate oxidative stress, thus offering new avenues for personalized skincare and anti-aging therapeutics. Issues for clinical application remain, including ethical concerns and a lack of robust validation frameworks, interdisciplinary collaboration, and appropriate education on AI methodologies. Given that aging-related interventions primarily target middle-aged to elderly populations, clinicians must be equipped to respond to patient concerns and communicate AI-generated insights effectively, fostering trust in AI-assisted anti-aging medicine.

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