Unlocking Longevity: How AI Health Agents Can Extend Your Life
Harnessing AI for Continuous Health Monitoring and Wellness
Imagine a world where your health is managed not by infrequent doctor visits but through a personalised digital advisor that monitors your well being 24/7. This is the vision presented in the research paper "AI Health Agents: Pathway2vec, ReflectE, Category Theory, and Longevity,1" by Melanie Swan and her colleagues. They introduce the concept of AI Health Agents—personalised digital advisors designed to facilitate "healthcare by app" rather than "sickcare by appointment."
It is expected there will more than 2 billion people over 65 by 2050. So need of such innovation has never been more pressing.
The Longevity Mindset
The paper proposes change in mindset when we think about longevity. First to treat aging as a disease, instead of an inevitable condition. A condition which can be treatable with potential interventions to slow, reverse and decline the process itself.
Second is to be pro-longevity tech. Meaning be welcoming in accepting and building technology for longevity like AI health agents and personalised aging clocks. Not only to individuals but large scale too.
The Power of Continuous Monitoring
Longevity agents or just health agents are future of personalised care. They are AI algorithms designed to take care of you. Focusing on maximising your lifespan(long life) and health-span(quality life). They continuous monitor parameters about your health. Parameters which are measured by number of devices for example smart watches, smart rings etc. Such interfaces communicate with you in natural language via app interfaces. And can take decision which is in best in interest of you.
On a population level they can help monitor thousands of patient simultaneously. They can reach individuals which are separated by the digital divide. Ensuring equitable access to these technologies will be crucial for transforming into a reality for all.
A future in which Precision Medicine as a service will be the norm.
The Science Behind AI Health Agents
These longevity agents have several different components. Which come together to make intelligent decisions. While continuous monitoring is one, the other is maintaining a digital twin of you.
A digital twin is a concept where physical entity or process is presented with virtual copy. In healthcare - a biomedical digital twin. They can be used to run simulations, education, research and drug development.
They combine and integrate data from various sources - such as biomarkers and aging clocks. Can find patterns which are hard to spot by traditional methods. Thus paving a way for precision medicine. Which can be tailored to individual needs.
The aging clock is a concept which compares biological age to chronological age. Thus measuring our rate of aging. Can possibly identify areas of interventions. They can analyse various factors like epigenetic, transcriptomics, glycan, metabolic and telomere length.
These agents would utilise generative AI, graph neural networks and knowledge graph embeddings to process vast amount of biological data and create comprehensive models of you.
Digital Biology and AI
By bringing Gen-AI methods to biology, it might become possible to explain biology via mathematics. Which hasn’t been the case till now. Pointing a future when AI can derive mathematical formalisation of biological processes.
Comparing to language models, biological AI models may be orders of magnitude complex. Examples of such models are Protein and Genome language models.
Blockchain for Securing Health Data
For storing and managing health data, it is proposed to use blockchain. The paper discuss projects like MediLedger and Triall. Demonstrating real world case scenarios of the application of blockchain technology in biology and healthcare.
Due to decentralised and encrypted nature, data on blockchain is inherently secure. Making it suitable for storing sensitive health data.
Biocurrency and Bio Wallet
The paper proposes a new system for tracking health parameters and interventions using blockchain. Representing biological entities like organs, biomarkers or interventions as “biowallets” with “biocurrency” balances. These biocurrencies maintain homeostasis. Homeostasis is process by which an organism maintains stable conditions and adjust it to best suit its survival. These biowallets will hold balance of the current state of biomarkers.
By tracking changes of biocurrency, it will be possible to constantly refine the protocols and projected outcomes.
Pathway2vec and Personalised Longevity Protocols
The paper introduces a method called “Pathway2vec”. It represents biological pathways vectors for ANNs(Artificial Neural Networks). Simply, biological information into numerical information so that AI systems can analyse. With goal to create personalised longevity interventions - Personalized Aging Clocks. This method is similar to word2vec which analyses language. It can also be used to represent genes, genetic variations, mutations and diseases. With core idea to slow down aging process and develop tailored personalised longevity protocols.
Risks and limitations of Health Agents
The most important will be how fast we can build such AI systems. It might be too late given the aging population. Other major risk is privacy and security of sensitive health data. How it gets processed and implemented by AI systems.
Conclusion and Takeaways
The paper presents a compelling take on how health agents can become part of everyday healthcare where personalised advice and continuous health management becomes the norm. With potential to significantly improve human lifespan and overall wellbeing of an individual.
Given above was a detailed interpretation of the article AI Health Agents: Longevity, Pathway2vec, ReflectE, and Category Theory. The article sits at convergence of AI, digital biology and blockchain technology.
This is an ongoing article, would appreciate any suggestions regarding mistakes or comments. Your insights will be invaluable in enhancing the quality of my work.
Swan, M., Kido, T., Roland, E., & dos Santos, R. P. (2024, May). AI Health Agents: Pathway2vec, ReflectE, Category Theory, and Longevity. In Proceedings of the AAAI Symposium Series (Vol. 3, No. 1, pp. 426-433).