Back in 2006, a website called “The Death Clock” appeared on the internet, with a promise to answer one of life’s greatest questions: “When will I die?” Since then, over 60 million people have used the site, which gives a somewhat grim countdown to the day they’ll meet their demise, or does it?
Far from being just a random number generator, The Death Clock is an early, if somewhat crude, way of utilizing data to predict lifespan. It analyzes information such as date of birth, lifestyle, gender, and location to generate its results in a semi-entertaining, although somber, way.
Of course, since 2006, more options have appeared on the playing field, such as a different Death Clock powered by AI. Its modern versions draw from larger levels of data and work with more sophisticated models, and this is where AI comes into play.
Market value
Just like the fascination with death dates drove internet traffic to that particular site in the 90s, so too does the interest in a healthier, longer lifespan drive longevity investment in 2024. AI is a tool to accomplish this.
According to data by Markets and Markets, the AI in healthcare market in 2024 is estimated to be $20.9 billion, with a rise to $148.4 billion within the next 5 years. This averages to a compound annual growth rate (CAGR) of 48.1%, showing a high level of confidence in the industry.
Its drivers are the growth in data volume and complexity, pressure to reduce healthcare costs, and the need for improvised healthcare services. Meanwhile, the usage of such technologies is mistrusted by medical professionals who believe them to be more hype than substance.
Statistics covering the AI and longevity market aren’t as easy to come by, primarily due to the relative newness of the field. The latest available studies by Allied Market Research show a suggested market value of $25.1 billion in 2020, with an estimated CAGR of 6.1% by 2030, giving a predicted value of $44.2 billion.
Where is all that AI money going?
With billions at stake, funding has poured into various areas across healthcare, longevity, and the research behind it all.
In healthcare, AI is used to:
- Manage and analyze patient data — language-based models can be used to transcribe consultations, while predictive risk models can help manage current and future healthcare needs.
- Analyze medical imaging and diagnostics — analytics models of AI are used in radiology and pathology to improve the cost and speed of diagnosis. Accuracy within this field remains a hotly debated topic.
- Drug discovery and precision medicine — AI can accelerate the development process and help tailor treatments to an individual using genomic therapies.
- Mental health and virtual assistance — just as chatbots have provided an instant support system for many companies, so too could they be integrated into healthcare outcomes. Although currently no commercial solution is a match for a human, these AI-based models could offer a lifeline at a time when connection is so valuable.
Within longevity, growth is expected across several general therapeutic areas:
- Senolytic drugs — These remove or alter senescent cells, which contribute to various age-related diseases. They may impact major age-related diseases that involve neurology, metabolism, and the cardiovascular system.
- Gene therapy — attempting to repair or modify genes or genetic components in order to slow down this aspect of aging. These therapies seek to impact the underlying genetic causes within aging as a whole.
- Immunotherapy — COVID-19 has done significant harm to the world’s health, the full extent of which is still yet to be determined. However, it has also boosted interest in immunotherapeutic solutions that can help improve immune function and fight age-related conditions.
- Biomarker discovery — research into biomarkers seeks to establish insights into biological and chronological age as well as to identify metrics for early detection of age-related diseases.
- Clinical trial optimization — real-time monitoring, big data analysis, and streamlining of trials are some of the potential benefits of integration AI solutions into scientific processes.
- Personalized longevity plans — similar to the approaches of precision medicine, personalized longevity seeks to address genetic and lifestyle factors in a longevity-focused approach to healthcare. These can range from personalized regimes to app-focused treatments to others.
These approaches do not exist in silos. Instead, they complement one another, with disciplines often crossing, and are by no means exhaustive.
Companies working on AI longevity solutions
Numerous companies around the world are working to integrate AI into their longevity solutions. These are some of the industry’s top players.
Insilico Medicine
Founded in 2014, this Hong Kong- and New York-based company uses AI technology to develop and accelerate therapeutics for age-related diseases. In 2023, it filed for an IPO and was valued at approximately $895 million according to Forbes. An early adopter of AI for medicine technology, the company kicked off discussions about the use of generative AI for drug discovery back in 2016. In 2024, it released the latest Phase IIa results of a proof-of-concept AI-designed drug treatment for pulmonary fibrosis.
BioAge Labs
US-based BioAge Labs is a biotech company that targets metabolic aging. It leverages machine learning, AI, and longevity science to identify and target biomarkers. Topping Crunchbase’s Longevity Start-Up list, this biotech underwent Series D funding recently and filed for an IPO in September 2024, which was estimated to raise $198 million to support the company’s current and future developments.
Altos Labs
Perhaps one of the most well-known names in longevity due to its famous founder, no other than Amazon’s former CEO, Jeff Bezos. However, fame and fortune can only fund progress, not buy achievements, and this company has made some notable ones. In August of 2024, it launched an AI and computational biology institute, which is set to address some of the industry disparity with AI skills, and in addition, it is continuing its mission of finding the so-called “fountain of youth” through longevity research and targeting fundamental aging processes at the cellular level.
California Life Company (Calico)
Bezos isn’t the only Silicon Valley face to back AI and longevity solutions. Calico is a subsidiary of Alphabet, the parent company of Google. Having received a recent investment of $2 million, the company has continued its research approach into the biology of aging using AI technology. In 2024, it was estimated to have produced revenue of $42.3 million.
Juvenescence
UK-based biotech Juvenescence is focused on using AI to develop therapies that target senescent cells. In doing so, it seeks to develop drugs that reduce cellular damage and enhance the human healthspan. According to statistics by Crunchbase, it is said to have received a total funding amount of $219.2 million.
Unity Biotechnology
Seeking to develop a new class of therapeutics to slow diseases of aging, Unity Biotechnology, also known as UNITY, reports a total of $294.9 million according to Crunchbase. Like BioAge, it’s also listed on the NASDAQ and trades under the ticker UBX. Its latest report notes that the company plans to continue its ASPIRE study to treat diabetic macular edema (DME), using UBX1325 (foselutoclax) a small molecule senolytic drug inhibitor which acts on the proto-oncogene protein c-bcl-2 inhibitors.
Perceptions of trust
According to a 2021 report from the Massachusetts Institute of Technology (MIT), trust in AI technology is approximately the same across most generations, with the majority saying they think AI is somewhat risky. However, when asked about the benefits, this opinion diverges. Generation Xers and Millennials were quicker to suggest its potential benefits than both Baby Boomers and Generation Zers.
When specifically looking at AI use in health care and caregiving, the majority ranked AI as “a little” and “somewhat” risky, while results for its benefits spread primarily across being “extremely” useful, “quite” and “somewhat”. This indicates a level of uncertainty in AI usage among the general public but with a piqued interest in its potential.
Diving further into the details of use cases within healthcare, people appeared skeptical regarding its usage when predicting life expectancy, while supporting its usage in more accurate medical record keeping.
With public opinion very much on the fence, it appears as AI is integrated into healthcare, there will be substantial challenges in building trust in platforms. This aligns with expert opinion on the matter, with ethical and trust concerns being raised, highlighting that there are genuine questions, such as accuracy, bias, transparency, privacy, and fairness, among others that need to be answered before AI can prove itself as a trustworthy tool within the scientific community.
Ethical concerns
As one famous movie said, “with great power comes great responsibility,” and that could not be more accurate when it comes to AI. Hidden among the excitement at a new technological approach is a mixture of fear and concern, not only for the practicalities and accuracy of the technology but also how ethical it is used. Some of these include issues surrounding:
Hallucinations and trust — Large language models (LLMs) are known to generate outcomes that are not always based on reality. These are commonly referred to as hallucinations. Knowing this possibility, the inclusion of predictive AI tools in particular should be checked thoroughly before implementation.
Data protection — laws such as GDPR, HIPAA, and patient-doctor confidentiality define trust in the healthcare world and beyond. AI, at its source, relies on data, so how can that data be protected? This is one of the major questions puzzling law makers, scientists, and medical staff alike.
Bridging a staffing gap — lack of medical staffing is a concern across the world, with World Health Organization (WHO) estimating a shortfall of 10 million healthcare workers worldwide. To put this in perspective, this is the entire population of Sweden, or more than the population of New York. AI’s integration in healthcare is intended to bridge the gap and relieve some of the workforce pressure. However, there are concerns about how this would be implemented and whether it would be accepted by the population.
Inequality — like the majority of healthcare solutions, it is suggested that lower- to middle-income countries could be faced with inequality when it comes to AI implementation. These concerns were expressed at the World Economic Forum Annual Meeting in 2024. Unreliable tools and inappropriate applications could drive even greater inequalities with some of the world’s strongest economies.
Accountability — in general research or medical practice, when it comes to accountability, there is a defined chain of responsibility both professional and ethical. When the stakes are high, it becomes more difficult to accept “the computer did it” as a reason, and if this is the case, who holds the responsibility?
Knowledge — as a modern technology, AI is still in a somewhat fledgling state, estimated to be somewhere between a clumsy toddler and awkward teen, depending on the expert. However, as it grows in usage, it raises the question of who is equipped to use it accurately and effectively. In 2022, Deloitte estimated the world’s total AI work-force to be at around 22,000. Although the situation can be suggested to have improved since then, both company leaders and experts agree that the current work-force is in need of serious up-skilling, and that’s before specializations in health or longevity.
Potential for results
As of 2024, it’s clear that AI is set to infiltrate our lives in a significant way, and the mass use of tools such as ChatGPT and other predictive models is evidence of this. However, when the stakes are high, as they are in longevity, a more cautious approach is needed.
To date, there are many promising AI-based approaches to healthcare and excellent examples of how it can be integrated into research, learning, and treatment. However, the lack of specialists, and the challenges in trying out new approaches and providing their scientific basis, mean that it is likely that commercially available trustworthy longevity outcomes could be a discussion for the near, but not immediate, future. What is more likely is the increased use of AI within data analysis with the careful scrutiny of human scientists.
