Darekar et al. studied the aging of the brain using artificial intelligence (AI), especially in people with mild cognitive impairment (MCI) and Alzheimer's disease (AD). The researchers used various AI models to predict brain age using MRI scans from 825 people to identify patterns related to normal aging and aging linked with MCI and AD. They used a deep learning model called AgeNet, integrated with tools such as SHapley Additive exPlanation, local interpretable model-agnostic explanations, and layer-wise relevance propagation to identify regions that were significant in predicting the age of the brain. Researchers found that AgeNet was the best AI model at predicting the brain age and identifying regions of the brain that are impacted by MCI and AD, surpassing traditional models. From the experimental data, MCI patients showed mild differences in brain regions compared to the cognitively normal participants. AD patients, on the other hand, displayed broader and more severe changes in comparison to individuals with normal cognition, highlighting the critical aging of the brain. These AI-driven tools prove to be promising technologies that can be used to make early detection of aging from the structural brain changes. They help in tracking neurodegenerative disease progression, with a potential to make personalized treatment plans for patients suffering from such diseases.