There is a quiet revolution happening which has the potential, perhaps more than any previous technology revolution, to benefit humanity profoundly. It’s the convergence of artificial intelligence (AI) and healthcare.

There is lot of debate about how AI can complement, change and enhance human intelligence. But as we seek to grow our minds, should we not also be seeking to advance, protect and understand our bodies?

I have been working on finding cures for diseases for my entire career. My excitement in AI is its potential to use new interactions between human and machines to unlock the most complex operating system in the world - the human body. AI’s advanced intelligence and reasoning can help us understand our bodies in ways we have never before managed, supercharge human creativity and help scientists to generate new medicines.

From a lifestyle point of view, intelligent augmentation is commonplace today. It happens every time we open our smartphones, search the internet and interact with devices in our homes. But there is one area where using AI will prove far more fundamental than simply enabling our lifestyles. This is the greatest unmet human need - the treatment of disease.

We are fortunate to live in a time when our lives will extend far beyond what we would have imagined 50 years ago. But while we may be living longer, we cannot necessarily expect to spend our later years as healthily as our earlier ones. Debilitating diseases such as cancer, Alzheimer’s and diabetes are already placing an unprecedented burden on families, society and the economy.

Image: Cancer Research UK

In a world where so much has been reimagined by technology, we should be asking: why are there are still around 9,000 diseases that have no treatment whatsoever? Why are there 300 million people suffering from rare diseases for which, under the current economic model, no drug will ever be developed? Shouldn’t technology be making a far bigger impact here?

Of course, we are already seeing AI making profound progress in some areas of healthcare. In diagnostics for example, where early detection and accurate diagnosis are advancing rapidly and augmenting doctors in ways they would have never conceived of five years ago. But that is only one part of the problem. Imagine being diagnosed with a disease, then being told there is no treatment for it.

The pursuit of treating disease is fraught with challenges. The lesson that I learned in biotech, before I founded my AI company, is just how big the challenge can be in pharmaceutical R&D. The derivative of this challenge is cost, time and a critical unmet need in untreated disease that has so far have been too tough to solve. It now costs an average of $2.6 billion to take a new drug from discovery to FDA-approval, a process that can take 10-15 years. This by itself is bad enough. What makes it worse is that 97% of all R&D programmes will fail.

Image: CSDD Tufts studies, 2014

It is not an easy problem to solve. Simply put, biology is very complex. Finding and understanding the underlying cause of a disease, let alone finding a cure for it, is incredibly difficult. The human body is the largest known data system with more than 37 trillion cells. It is the end result of millions of years of evolution and an infinite number of factors and permutations. The deeper we go in studying the human body, such as sequencing the human genome, the more we realise we have so much left to learn.

The problem is not just the complexity of biology. It is that we, as humans, are limited by the amount of information that we are able to absorb and process. To give you a sense of just how much information is out there, consider that in the biomedical domain alone, there are 10,000 new scientific papers published every day, more than 100 million scientific patents, hundreds of deep and rich chemical and genomic databases, and tens of thousands of patient clinical trials datasets. This is not something human beings are designed to process.

What is overwhelmingly clear is that there are many problems that are so difficult in science that our own human thinking abilities have not evolved to solve them alone. But as humans are clever, we have invented new thinking technologies that can work alongside us to solve these very large problems.

That is exactly what I was trying to achieve in my own company. I wanted to have a conversation with an AI trained in human biology. I wanted to say “computer, find me a cure for ALS”. Then the computer would read every piece of data, every scientific paper, every chemistry database, every genomic database - everything. The AI would be able to contextualise what that information and data means. It would be able to reason, understand and postulate the underlying molecular cause of ALS... and bang, the AI would show me how to design a drug that treats it.

While that conversation might be a little way off, it is not as far away as many of us think. A lot of people are talking about the narrative of AI healthcare in the future tense, but the reality is that it’s happening now. AI technology is making the leap from being passive to being generative. Generative AI tools use computers and algorithms to reason and come up with explanations by themselves. All they need are our goals and our constraints.

So just where are we on the AI healthcare spectrum?

We, as scientists, mathematicians and computer engineers have made algorithms that are much smarter than we are at certain tasks, but in other tasks they are nowhere near as smart as humans, because they don’t need to be. Your calculator is smarter than you are in arithmetic. An NMR AI is smarter than you are in spatial and molecular profiling and can predict distance between chemical bonds better than you can. A knowledge graph with billions and billions of data points knows more biology than you do.

We haven't yet hit the top arc of innovation and exponential advancement in biology and life sciences through the use of AI technology. However, we are at a point now when the combination of advanced algorithms, greater processing power and access to vast amounts of data is enabling scientists and machines, working together, to develop profound insights into biology and disease at a level far beyond what is possible for humans alone.

In biology and life sciences, we are about to experience an extraordinary period of innovation, at the pace we’ve seen in other data-rich fields, powered by advances in computer science and engineering. In next five years, we are going to see more transformation in healthcare than in the previous fifty. Much of that transformation will come from technology, and much of that technology will be AI-driven.

With the help of AI, we are at the start of an unprecedented renaissance in the science of human health. In just the past few years, we're beginning to understand diseases that we have struggled with for ... well, forever.

As a society, we have a choice in how we use this powerful technology. We can choose to be scared of AI. Or we can use AI to help us, to augment and collaborate with us, to overcome our cognitive limitations, to aid us in what we as humans do badly and to enable us to do what we want to do, better and faster. And as we discover new ways to give machines intelligence, we can imagine a world where we distribute that intelligence to every scientist around the world, the way electricity was distributed across a network to power work.