Why AI in Health is Scary but Important

OK, so we’re not talking Terminator scary. Or HAL 9000.

But putting our faith into a machine which can essentially make life or death decisions is no longer something we find only on Star Trek. Software as medical devices that make diagnostic assertions, swaying a doctor’s opinion or perhaps even contributing to confirmation bias, or make conjectures about where the optimally place an ambulance unit for shorter response times – all of these things are either already on the market or being developed right now.

Not scary enough? How about machines teaching aspiring surgeons on how to do surgery? Touch Surgery – now acquired by Medtronic –  does just that and has created amazing traction amongst the medical community in a very short period of time. It does not take a genius to figure out where this is going… With human-led remote surgery becoming common practice, it is only a matter a time before fully automated robotic surgery for pretty much every procedure becomes a reality.

You will be forgiven for keeping a lid on a minor panic attack. Don’t fret… it’s not quite as I am Robot as you might be inclined to think. Hollywood does a pretty poor job representing reality. And if you think Hollywood is bad, you should check out some of the SciFi B-movies on Bollywood channels. AI, machine learning – automation in general – gets a pretty bad rep, but it has actually been around for years. From the ventilators that have saved so many thousands in the past few months in the global pandemic, to blood flow control systems used in major surgery. It is already ubiquitous.

The Cost of Doing Nothing

AI in healthcare market size

Market growth in all areas of AI is projected to remain strong for the next 5 years. The COVID19 emergency lends even greater weight to medical AI as a high growth market, with an uptick of approximately $4.5B in 2020 alone. The image above shows the growth of global AI markets before the global pandemic (Sources: Morder Intelligence Report, TechRegister, MedGadget, BI Research). It has become very clear in the last few months that automation and AI will have to play a critical role health system’s recovery, not least because the backlogs that already existed for critical illnesses, such as MRI scans for cancer, have only got much, much longer. Radiology in particular is a global scarcity already. The NHS already outsources £165M of radiology work – a number that is exacerbated by chronic shrinkage of radiology specialists. In 2018, according to RCR, hospitals reported 379 unfilled consultant radiologist posts across the UK, two thirds of which were vacant for at least a year.

As the COVID crisis has shown us, being under-prepared for emergency costs lives. And actually it costs political reputation too, and therein perhaps lies a nugget of fortune. The UK hasn’t exactly been a beacon of hope in 2020, but if there is one thing going for this country it is a healthy set of strong SME’s backed by solid investment (though there was a wobble there too; a story for another day) in healthcare and the government does seem to be supporting this from the top. Matt Hancock – despite failures during the pandemic – remains a strong advocate for tech in healthcare. Government funding bodies such as InnovateUK have moved very quickly in supporting R&D heavy businesses  and of course academic institutions. It would not be surprising at all to see within the results of these investments next year how many AI companies have benefited and created positive value to healthcare.

Whether it’s a start-up analysing blood factors for better diagnosis, or drug discovery, or bone fracture detection – the question really isn’t about whether we can use AI for these. Of course, the answer is WE CAN. The real question is, how do we validate outcomes? How do we know the machine is better than a human? How can we quantify and predict edge cases causing spurious results (and whether they are in fact worse than a tired doctor making a poor or emotional decision)?

These are the big questions to which we will attempt to put some answers in a series of blog posts. Watch this space!

Manish Patel

CEO @ Jiva. Stringer of numbers in complex patterns.

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