Artificial intelligence in predicting biological age

This video summarizes the review “Application of AI in Biological Age Prediction” by Dawei Meng, Shiqiang Zhang, Yuanfang Huang, Kehang Mao, and Jing-Dong J. Han (Current Opinion in Structural Biology, 2024; PMID 38310737). The paper discusses how artificial intelligence and machine learning models, ranging from traditional regression to deep neural networks and Transformers, are transforming biological age estimation. It compares omics-based clocks (epigenetic, transcriptomic, proteomic, metabolomic), single-cell ageing clocks, and non-invasive imaging approaches, highlighting how deep learning enhances accuracy, interpretability, and scalability. The authors also outline key challenges, including data integration and model transparency, that must be addressed for clinical translation.