AI fast-tracks human longevity extension

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Top longevity leaders frame how next-gen AI can extend lifespan.

Deep Longevity, which specialises in the development and the application of next-generation AI for aging and longevity research, has announced the publication of an article in Nature Aging titled Artificial Intelligence in Longevity Medicine, written by Alex Zhavoronkov, Evelyne Yehudit Bischof and Kai-Fu Lee.

Longevity.Technology: Longevity and AI are deeply enmeshed; from accelerating innovation and technology transfer, to developing personalised health therapies, the presence of AI is a key factor in extending lifespan and healthspan and ensuring maximum wellness. Next-generation AI could not only improve longevity investigative strategies and research, but push them in entirely new directions – vive la révolution!

Hong Kong-based Deep Longevity was spun out of Insilico Medicine and quickly acquired by Regent Pacific. It develops explainable AI systems to track the rate of aging at the molecular, cellular, tissue, organ, system, physiological and psychological levels, as well as developing systems for the emerging field of longevity medicine. Creators of deep aging clocks that leverage data from multiple biomarkers, Deep Longevity, through a research partnership with  Human Longevity, Inc, provides various aging clocks to physicians and researchers.

Zhavoronkov, Bischof and Lee describe a new field of study which brings together AI, research and medicine – longevity medicine. This definition is expanded by the authors as the preventative and restorative medicine enabled by the deep aging clocks and AI.

In the article the authors describe the basic framework for the application of deep learning to longevity research and the opportunities for longevity medicine in clinical care and the longevity industry.


“…The ability to track and learn the minute changes that transpire in human body every second over the patient’s lifetime and in large number of patients enables the development of a new field of medicine…”


Rather than tackling individual diseases, longevity medicine would overview the myriad age-associated processes and diseases that manifest in unison in later life, the main cause of which is aging itself.

By harnessing the power of AI to spotlight the features of aging, spot patterns, learn the basic biological and physiological processes that occur with the passage of time and predict aging and resultant pathologies, lifespan and healthspan could be expanded. AI has the ability to crunch the enormous and ever-growing longitudinal data sets by using deep neural networks (DNNs).

“Artificial intelligence holds great potential for medicine in general; however, the ability to track and learn the minute changes that transpire in human body every second over the patient’s lifetime and in large number of patients enables the development of a new field of medicine – longevity medicine”, said Evelyne Yehudit Bischof, a physician at Human Longevity, Inc, and associate professor at Shanghai University of Medicine and Health Sciences.

Next week our Managing Editor, Margaretta Colangelo, will be discussing the impact of the paper with co-author with Alex Zhavoronkov, and finding out what consequences it might hold for the longevity space – stay tuned!

Eleanor Garth

Editor

Now a science and medicine journalist, Eleanor worked as a consultant for university spin-out companies and provided research support at Imperial College London and various London hospitals in a former life.

With a keen interest in all things geroscience, Eleanor covers the biological mechanisms of aging, highlighting how interventions – from nutritional strategies to advanced therapeutics – can influence the aging process at a cellular level. She also investigates the broader implications of aging research, including the integration of longevity science into healthcare strategy and the expanding wellness economy. Her work (hopefully) provides insights into how these developments are shaping the emerging landscape of healthspan optimization and age-related innovation.

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