Longevity Med Summit 2026 – Lisbon
Longevity Med Summit 2026 – Lisbon
The Longevity Med Summit is a leading global platform advancing longevity medicine, health optimization, and evidence-based wellness. It brings together pioneers in biotechnology, regenerative therapies, digital health, nutrition, and supplements to shape the future of healthspan and preventive healthcare.
The 2026 editions mark the 4th summit in Dubai and the 5th summit in Lisbon, gathering top physicians, researchers, wellness leaders, and innovators from around the world.
Highlights include:
A 360° view of innovations transforming health and longevity.
Networking with global exhibitors across eight key sectors.
Cutting-edge sessions on biotechnology, regenerative medicine, digital health, nutrition, and preventive strategies.
Exclusive pre- and post-event experiences curated for delegates.
Why Attend:
The summit offers a unique opportunity to:
Gain insights into breakthrough longevity and health optimization technologies.
Connect with international leaders in science, business, and wellness.
Expand networks and business opportunities through exhibition and sponsorship.
Experience curated travel and networking events in Dubai and Lisbon.
Venue (Lisbon):
Pavilhão Carlos Lopes
Av. Sidónio Pais 16, 1070-051 Lisboa
Organizer Contact:
Website: Longevity Med Summit
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
Research
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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