In March 2023, MIT Technology Review revealed that Sam Altman, the CEO of OpenAI (ChatGPT), was the mystery investor behind the $180 million investment into stealth startup Retro Biosciences, a biotech company with the ambition of “adding 10 years to the human lifespan.” This investment marks the latest tech entrepreneur expressing their interest in longevity science and a new connection with innovative AI technology.
According to February 2023 reports, AI is continuing to gain traction in healthcare applications. Currently, the market is estimated at $14.6 billion (USD) with a compound annual growth rate (CAGR) of 47.6%, with solutions spread across various healthcare fields, such as patient data and risk analysis, precision medicine, cybersecurity, lifestyle management, and drug discovery.
AI is currently being used in longevity and healthcare
The increasing convergence of AI technology and longevity science is sparking advancements in the sector, with established businesses, start-ups, and researchers utilizing the technology. Most recently, scientists explored how ChatGPT, an AI-based language model, was able to predict Alzheimer’s in 80% of cases when analyzing speech. However, it is not the only implementation.
Due to its capabilities of analyzing a vast range of data, AI is proving instrumental in the discovery and development of new compounds. One such application is Insilico Medicine’s ChatPandaGPT integration, which allows researchers to ‘talk’ to its PandaOmics target discovery platform, thus analyzing and navigating large datasets in order to discover new biomarkers and therapeutic targets.
Atificial intelligence models are currently being used to conduct genomic analysis and identify specific genes associated with healthy human lifespan. One such project is Calico Labs’ collaboration with the well-known platform AncestryDNA, which analyzes a vast range of data to establish hereditary factors in longevity.
By using AI technology, researchers may be better able to detect iomarkers for disease early, facilitating prompt interventions. BioAge Labs’ partnership with Age Labs AS seeks to analyze samples and health records from the Nord-Trøndelag Health Study (HUNT) biobank to develop novel therapeutics.
The precise impact of diet and exercise on the individual level is still not completely understood, and AI may change that by analyzing a wider range of data. Nutrino’s personalized AI platform is a predictive glycemic response algorithm that can help optimize eating habits and potentially reduce diabetes.
On a similar note, pecision medicine allows for tailored medical solutions. AI can be employed to analyze relevant data and help design and deploy these strategies. For example, Deep 6 AI’s clinical trial matching system connects participants, patients, and researchers for clinical trials in order to broaden databases.
Occasionally, AI has been known to spot what a human cannot due to its ability to analyze data more closely and at greater volumes. Zebra Medical Computer Vision AI medical imaging tool can be used to analyze data, including medical imaging, to diagnose diseases, such as bone, liver, lung, and cardiovascular illnesses. This start-up was recently purchased by Nanox for a rumored sum of $200 million.
Using AI in longevity research
Recent advancements in the sector and widespread application across various industries have shown the technology to be effective. Although each AI implementation is different, in longevity, it is finding its application due to its capabilities for analyzing and working with the immense range of data in the healthcare sphere, allowing researchers to identify patterns, relationships, and evaluate factors in age-related diseases. In turn, they are better able to develop potential solutions and test them, at least in the initial stages, to ensure their feasibility.
Despite the achievements, AI isn’t a flawless solution, and people who apply the technology in their work are advised to do so with caution to ensure that any solutions created operate efficiently and can be used responsibly. For example, biased data introduced into AI datasets can discriminate against or favor certain groups. In addition, such models may contain incomplete or inaccurate data, making them ineffective.
For AI in longevity to work effectively, it must have access to expansive datasets. This creates an issue wherein sensitive data could be breached or exposed. In addition, it raises concerns of consent, as people may be unaware of when and how their data is used. These risks could be minimized by the responsible application and security of AI.
Although this isn’t exclusive to AI, the risk of geographical and economic disparities in access, as with most state-of-the-art technology, means that access may be limited. This raises ethical concerns about how datasets are used, whom they are used for, and who will benefit from the solutions. As artificial intelligence technology advances, more benefits and challenges to its usage will become apparent.
Recent implementations of artificial intelligence in the longevity sector have presented some impressive results, including the power to harness data in order to deliver tangible suggestions for therapeutics development and to analyze medical images. As AI continues to advance, its potential appears to grow in tandem. However, scientists should be careful to avoid data-related ethical concerns and pitfalls when onboarding this new technology.