Life Summit 2026
Life Summit 2026
The Life Summit returns for its second edition in Berlin, bringing together over 150 experts, 100 exhibitors, hands-on masterclasses, and live demonstrations to explore the future of longevity, preventive health, and human enhancement.
Following the success of the inaugural 2025 summit, this two-day event will feature:
Presentations from world-class researchers, entrepreneurs, and visionaries in longevity science.
Cutting-edge innovations in therapies, biotechnology, and preventive health.
Networking opportunities across the global longevity ecosystem.
Live tests and interactive masterclasses showcasing breakthrough technologies.
The 2025 edition featured leading voices including Dr. David Luu (Longevity Docs), Dr. Eric Verdin (Buck Institute for Research on Aging), Bryan Johnson (Blueprint), Rhiannon White (Clue), Prof. Dr. Andreas Michalsen (Charité), and many more — setting the stage for an even more ambitious 2026 program.
Target Audience:
Healthcare professionals, scientists, entrepreneurs, investors, technologists, startups, and all stakeholders in longevity and preventive health.
Organizer Contact: info@lifesummit.berlin
AT World does not take responsibility for any changes to this event, which we have published in good faith. Please direct any queries to the organiser.
News
Mole rat gene extends mouse lifespan
News
AI can predict Alzheimer’s with almost 93% accuracy, researchers say
Alzheimer’s AI can predict the disease with nearly 93 per cent accuracy using more than 800 brain scans, researchers say.
The system identified anatomical changes in the brain linked to the onset of the most common form of dementia, a condition that gradually damages memory and thinking.
The findings build on years of research suggesting AI could help spot early Alzheimer’s risk, predict disease and identify patients whose condition has not yet been diagnosed.
Benjamin Nephew, an assistant research professor at the Worcester Polytechnic Institute in Massachusetts, said: “Early diagnosis of Alzheimer’s disease can be difficult because symptoms can be mistaken for normal ageing.
“We found that machine-learning technologies, however, can analyse large amounts of data from scans to identify subtle changes and accurately predict Alzheimer’s disease and related cognitive states.”
The study used MRI scans, a type of detailed brain imaging, from 344 people aged 69 to 84.
The dataset included 281 scans showing normal mental function, 332 with mild cognitive impairment, an early stage of memory and thinking decline, and 202 with Alzheimer’s.
The scans covered 95 of the brain’s nearly 200 distinct regions and used an AI algorithm to predict patients’ health.
Being able to use AI to help diagnose Alzheimer’s earlier could give patients and doctors crucial time to prepare and potentially slow the progression of the disease.
The analysis showed that one of the top predictive factors was brain volume loss, or shrinkage, in the hippocampus, which helps form memories, the amygdala, which processes fear, and the entorhinal cortex, which helps provide a sense of time.
This pattern held across age and sex, with both men and women aged 69 to 76 showing volume loss in the right part of the hippocampus, suggesting it may be an important area for early diagnosis, the researchers noted.
However, the research also found that the way brain regions shrink differs by sex.
In females, volume loss occurred in the brain’s left middle temporal cortex, which is involved in language and visual perception. In males, it was mainly seen in the right entorhinal cortex
The researchers believe this could be linked to changes in sex hormones, including the loss of oestrogen in women and testosterone in men.
These conclusions could help improve methods of diagnosis and treatment going forward, Nephew said.
More than 7.2m Americans are living with Alzheimer’s, according to the Alzheimer’s Association.
More research is being done to reveal other impacting factors.
Nephew said: “The critical challenge in this research is to build a generalisable machine-learning model that captures the difference between healthy brains and brains from people with mild cognitive impairment or Alzheimer’s disease.”
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