AI is HOT right now. A lot of money and (so far, mostly human) brains are being put into developing ways of using AI in all sorts of industries: finance, logistics, manufacturing – it’s actually quite hard to find an area of human endeavour which someone, somewhere is not trying to build an AI system for.
One of the areas where AI has been generating the most amount of interest is healthcare. Indeed, Channel 4 News did a nice piece about AI in medicine this week, featuring the likes of Google Deepmind and a lot of visual imagery of glowing numbers cascading down hospital curtains.
Graphic designers: still stuck in the Matrix
There are a lot of people in healthcare who aren’t sure what AI is and what it might mean for their everyday work. I’m not an expert by any means, but I thought that a gentle introduction might be useful for all the doctors, nurses, managers, therapists, pharmacists (and all the many other people working in healthcare) who want to know a bit more about what this means.
Firstly, some terminology. “Artificial intelligence” is a general term for building computer systems to do the things that our human brains are good at: solving complex problems, recognising patterns, communicating through speech, making forecasts about the future and so on.
“Machine learning” is one example of a method used to build AI systems. The central idea here is actually quite intuitive (and the clue is in the name) – it’s all about learning. How are we able to drive a car, speak English, French or Japanese, create a beautiful work of art? These skills are not hard coded into our brains, or activated in an instant when we reach certain ages: we learn them over time though interacting with and sharing data with the world around us. Machine learning takes the same principle and applies it to computers. Instead of programming computers to do things based on fixed rules (“If yes in English then output oui for French or はい for Japanese”) machine learning involves feeding in data and training the computer to do the thing we want it to do. If we do this well, then we end up with an algorithm (a set of instructions or actions) that does something useful – recognising faces in photos for example, or translating languages. Some of the most exciting recent advances in AI have come about from new techniques in machine learning.
The critical thing about machine learning is that you need data to train these algorithms: often a lot it. Data is the fuel for making useful machine learning algorithms.
So what does this mean for healthcare? Well there are a few reasons why healthcare is fertile ground for AI, and machine learning in particular:
- Healthcare is full of complex problems and pattern recognition. The classic example here is the process of making a diagnosis, based on interpeting a rag bag mix of language data (symptoms) and quantitative data (lab tests and imaging). In the past humans were able to solve these types of problems much better than computers – but these are things that AI is getting increasingly good at
- Healthcare is full of data – from electronic healthcare records, to lab data, to administrative data, healthcare is heaving with data. Machine learning algorithms will be at home here
- Healthcare is expensive. Rich countries spend something like 10% of their GDP on healthcare – globally the amount of money spent on healthcare is probably north of $10 trillion per year. Most of this is spent on people (roughly 60% of the NHS budget is spent on staffing costs for example), since healthcare is mostly a people business. There is therefore a very strong economic drive to automate some of the things in healthcare that people currently do
- Healthcare has lots of scope to be, well…better. If we invented no new drugs or medical devices for the next 10 years, and just tried very hard to apply the things we know work, reduce the number of errors and mistakes, close inequalities in access and provision – basically improve the quality of existing healthcare – then we could achieve vast improvements in patient outcomes. Healthcare is a long way from being optimised, meaning that there is lots of room for new ways of doing things (such as AI) to make healthcare better
Making all this happen is however, another matter. From a technical point of view, the role of AI in healthcare is still very limited, but is moving fast. Some areas of healthcare are going to be affected quicker than others. Most of the biggest recent advances in machine learning have been in language processing and image recognition – I suspect the first machine medics are going to be graduating in radiology (…and working 24/7, for no pay). I also think that there are whole swathes of healthcare that are currently off the radar of the likes of Google Deepmind, but where the biggest gains could me made: managing and improving healthcare services for example.
The rise of AI in medicine is also going to raise all sorts of issues. What are the ethical implications of a world where algorithms are making medical decisions? What does this mean for legal liability and regulation? What new skills and knowledge will the people working in healthcare need? What do patients want and who is going to be in control? Who is going to own and profit from this? What are the implications for how we collect and use sensitive healthcare data? If data is so central to all this, do we need to be investing in collecting better data? I hope to explore these topics, and more, in future posts.