21 June 2021
Longevity briefs provides a short summary of novel research in biology, medicine, or biotechnology that caught the attention of our researchers in Oxford, due to its potential to improve our health, wellbeing, and longevity.
A couple of weeks ago, we had the opportunity to sit down with Nikhil Yadala, data scientist and co-founder & CEO of Healome, to chat about the future of healthcare and artificial intelligence.
Healome is a service allowing users to store their healthcare information and bring structure to the unstructured data. Using artificial intelligence and machine-learning techniques, Nikhil and his team anonymise your data and enable healthcare practitioners and researchers to provide insights to enhance you and your family’s well being.
In the full talk, Nikhil discusses the impact of artificial intelligence on the future of healthcare, and how Healome is aiming to be the centre for personal health tracking and insights, in the future.
Here’s what was said:
Chris: What are two or three things that you intrinsically believe are inevitable for the future of you industry, artificial intelligence and healthcare?
Nikhil: First and foremost thing there is going to be very good improvements with respect to accelerating the pipeline of drug discovery by using AI and machine learning algorithms. If you think about the problem of drug discovery, it’s basically a problem of such in chemical space.
We want to design certain molecules with certain characteristics, with certain physical and chemical properties. These are just constraints. So when we can draw parallels to the problem of such like how such Indians tackle it, so you get a queery, which is just four to five words often, and that’s not even to medically correct most often. Within a few milliseconds, the search engines are able to collate the best ten articles for you by searching all over the internet from petabytes of the internet index. All of this is happening within few millisecond searching through the entire internet, which means that there have already been software systems developed to the extent that they can do this inference very quickly. And also with very good accuracy of the results.
Now an LP is of course, something that so it’s basically in natural language and we know the syntax of natural language. So it’s definitely a much more easier problem than understanding the language of the body and the genetics. And all this, but I think all the learnings that as a field, we have accumulated in delivering software systems in the problem of search would eventually be applied to the problem of drug discovery as well.
Another thing with respect to healthcare, it’s bit farfetched, but with all that is happening these days, I think that going forward, it will be a common thing where people maintain ledgers of their healthcare data, and the information of both how a piece of info about how that piece of data has flown from point A to point B would be minted onto a blockchain in a decentralised manner. It basically gives a proof of how on which all parties have access to a piece of data. And also it gives the ownership of the data to the users themselves. So I think it’s eventually going to go there.
We already have a lot of applications on a hyper-ledger’s both like news articles and also with the recent NFT, maintain non fungible tokens for so I think going forward the next step to apply all of these technologies is to healthcare.
Now I also like one other thing I Think is going to happen is we are going to have a much better understanding of how to tackle aging as a disease. Currently the biomarkers that track to measure, like even the methylation clocks or those which look good are even more deeper things., they’re not easily accessible to everyone. I think going forward, we’ll be in a position to at least approximately measure how good is that an intervention is doing with respect to increasing the health span or reversing the age?
One thing I always keep thinking about being from a computer science and technical background: we are going to reach longevity, escape velocity in our lifetimes, most probably. When that happens, most of the computer systems today are all about making sure that it is speed and making sure that it has very less latency in solving. To the extent that we are even often find with deploying software systems, which are just approximate solutions to that exact solution, but the exact problem, just because it can be computed in a much faster way, on a distributed way and with less latency.
I think that then we reach the longevity escape velocity. I’m not just talking in terms of healthcare in general, broadly about software systems and utilization of AI itself. We need not care much about speed and agility of development in this companies, which use AI. Currently every company or startup that uses machine learning they have all these timelines set up who gave you to deliver to certain accuracy, with the problem that you’re tackling by this month, that forces them to be more agile and that forces them to have more restrictions in the space of models that they can actually experiment with.
When speed, and time is no more problem than speed is also no a problem. I’m more interested to look how software systems and companies in general would evolve to that situation.
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