Modern health care faces several serious challenges, including an ageing population and
its inherent burden of chronic diseases, rising costs and marginal quality metrics. By …

This systematic literature review employs the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …

Clocks that measure biological age should predict all-cause mortality and give rise to
actionable insights to promote healthy aging. Here we applied dimensionality reduction by …

Short-term mortality risk, which is indicative of individual frailty, serves as a marker for aging.
Previous age clocks focused on predicting either chronological age or longer-term mortality …

Adverse childhood experiences (ACEs) are key modifiable risk factors for mental illness. The
potential to detect and mitigate ACEs to improve population mental health has led to large …

Advances in artificial intelligence (AI) have achieved expert-level performance in medical
imaging applications. Notably, self-supervised vision-language foundation models can …

The field of aging and longevity research is overwhelmed by vast amounts of data, calling for
the use of Artificial Intelligence (AI), including Large Language Models (LLMs), for the …

Biological age as an integrated value of biophysiological measures has been widely
investigated as a biomarker of ageing. It outperforms chronological age in predicting the …

Major depressive disorder accelerates DNA methylation age, a biological aging marker.
Subclinical depressive symptoms are common, but their link to DNA methylation aging in …

Clinical and biological information in large datasets of gene expression across cancers
could be tapped with unsupervised deep learning. However, difficulties associated with …