Fortune | FORTUNE 10月30日 18:14
AI赋能衰老研究,数据挑战与性别差异是关键
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人工智能正为延长人类寿命和提升健康水平带来新机遇,尤其在衰老科学领域。然而,数据是AI发展的关键瓶颈。目前,关于细胞和器官衰老机制、性别、种族及环境因素如何影响衰老过程的数据存在严重不足。专家指出,现有的医疗框架收集数据范围不够广泛,且通常在人们年老时才进行筛查,而衰老研究的目标是尽早干预。Hevolution基金会正与科研机构和生物技术公司合作,加速开发旨在延长健康寿命的药物。AI也被广泛应用于药物研发,许多项目专注于同时治疗疾病和衰老。尽管短期内不太可能出现“奇迹”,但未来20年内,衰老研究有望迎来重大突破。同时,大型语言模型在解析衰老科学知识方面潜力巨大,但填补数据空白,特别是关注女性、少数族裔等群体的数据,至关重要,因为不同性别在衰老过程中的健康状况和需求存在显著差异。

💡 AI在延长寿命和提升健康方面潜力巨大,尤其在衰老科学领域,但数据是关键瓶颈。目前关于衰老机制、性别、种族和环境影响的数据不足,现有医疗框架收集的数据范围不够广泛,且筛查时机较晚,阻碍了早期干预。

🚀 Hevolution基金会等机构正积极推进衰老研究,通过与科研机构和生物技术公司合作,加速开发延长健康寿命的药物。AI在药物研发中扮演越来越重要的角色,许多项目致力于寻找能同时治疗疾病和延缓衰老的药物。

⏳ 尽管近期内“奇迹”可能不多,但未来20年,衰老科学有望迎来重大突破。大型语言模型也有望帮助解析复杂的衰老科学知识,但前提是必须填补关键的数据空白,特别是关注性别差异。

♀️ 性别差异在衰老研究中尤为重要。研究表明,女性在生命中不同阶段的健康状况与男性存在差异,衰老研究和干预措施需要更精细化,以满足女性“重塑健康”而非仅仅“延长寿命”的需求。

Our never-ending quest to live longer and healthier lives is set to get a big boost from AI technology. But as with all things AI-related, one of the biggest roadblocks is data. 

When it comes to aging science, there’s a dearth of data to help scientists understand how cells and organs in the body age, and how differences in gender, ethnicities, and environments can affect the aging process, said panelists at the Fortune Global Forum in Riyadh this week.  

“Data is the key. The depth of biological data, the depth of demographical data, the depth of epidemiological data has to be properly collected,” said HRH Princess Dr. Haya bint Khaled bin Bandar Al Saud, senior vice president of research at Hevolution Foundation, a non-profit that focuses on aging science. But the current healthcare framework means the net we’re casting to collect data isn’t wide enough, she said.

“Screening usually happens when people get older. But what we are trying to do—all of us sitting here—is understand the biology of aging so that we can intervene as early as possible,” Dr. Haya bint Khaled bin Bandar Al Saud said. “No one can say how young we should start screening for aging. But aging starts at a very young age, it’s not when you hit 60.”

Hevolution is working closely with scientists and biotech companies to advance drug discoveries focused on “healthspan”—reducing age-related diseases—and on accelerating consumer access to these advances. 

Many labs today are using AI to speed up the drug discovery process, and make more bets. Alex Zahvoronkov, the founder and CEO of Insilico Medicine, said he has 30 AI-based projects underway that are specifically chosen for their “dual purpose” potential.

“We’re looking for protein targets and drugs that may work on a disease and work on aging at same time,” Zahvoronkov said. The first AI-discovered drug that gets approved to fight a disease and subsequently is able to demonstrate credible reversal of aging biomarkers in a real clinical trial will be a major milestone, he said.

“There is a lot of evidence suggesting we are on the cusp of major scientific discoveries in aging research,” Zahvoronkov said. “I don’t think you should expect to see multiple dramatic miracles in the next 10 years, but in the next 20 years… we are going to have many many dramatic events.”

Sophie Smith, the founder and CEO of NABTA Health, said that large language AI models have the potential to unlock important knowledge and insight in aging science. But as AI plays a bigger role, the importance of filling in “data gaps” becomes even more critical. 

Up until 1993, for example, clinical trials involved only male participants, and the vast majority of trials today take place in Europe and the U.S., she said. That means fewer than 1% of clinical trial participants today are women of Middle East, South Asian, and African origin, Smith said.

That’s especially problematic for aging science given that men and women age differently. Smith noted that a current assumption in aging science is that if a person lives to be 80 years old, 60 of those years may be healthy, and the last 25% will be in relatively ill-health. 

“But this assumption only applies to men,” Smith said. Women typically spend the middle 60% of their lives in relatively ill-health, due to hormonal changes, diagnostic delays, and other factors, and the last 30% of their lives in relatively good health. 

So research and advances in aging science need to take into account that for women “it’s not about extending life, it’s about reclaiming it.”

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AI 衰老科学 健康寿命 数据挑战 性别差异 药物研发 AI in Aging Aging Science Healthspan Data Gaps Gender Differences Drug Discovery
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