Blog summary of an editorial published in Aging’s Volume 15, Issue 8, on April 3, 2023, entitled, “Artificial intelligence and the aging mind.”

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Aging is a risk factor for many diseases, including Alzheimer’s disease (AD). While scientists have made some progress in understanding the physiology of aging and its relationship to AD and related disorders, our understanding remains incomplete (to say the least). It is possible that civilization is currently in the midst of an artificial intelligence (AI) and machine learning (ML) “boom.” Researchers are now using AI and ML technologies to elevate our comprehension of aging and aging-related diseases.

“Artificial intelligence (AI) and machine learning (ML) technologies can help us better understand these diseases and aging itself by using biological data from the brain or other sources to create a mapping between age and biological data.”

In a new editorial paper, researchers Jeyeon Lee, Leland R. Barnard and David T. Jones from the Mayo Clinic in Rochester, Minnesota, discuss a recent study they conducted and explore the potential of AI to revolutionize the field of geriatrics. Their editorial was published in Aging’s Volume 15, Issue 8, on April 3, 2023, entitled, “Artificial intelligence and the aging mind.”

Full blog - https://aging-us.org/2023/05/the-brain-age-gap/

Paper DOI - https://doi.org/10.18632/aging.204644 (PDF)

Corresponding author - David T. Jones - [email protected]

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Keywords - aging, brain age, artificial intelligence, Alzheimer’s dementia, neurodegenerative disease, biomarker

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Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways.

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