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Manufacturing technology


Improvements in AI and machine learning technology mean it could soon be found across the supply chain.


$2.1bn


Foreign direct investment that funds India’s over 4,000 medical technology start-ups.


Business Today $31.3bn


Growth of the healthcare AI market projected by 2025, at a CAGR of 41.5%.


Bloomberg 54


Yet if medical devices are now being pumped out in their billions – the EU, for its part, sells over 500,000 different models – the way they’re made hasn’t totally escaped the pattern of their ancient predecessors. The artisanal workshops may have been replaced by assembly line factories, but many medical devices are still fundamentally built with humans at their heart. Whether it’s the people on the shop floor searching for hairline cracks or those spending hundreds of hours in front of computer screens sifting through data to understand a problem, supply chains still rely on manual oversight to run smoothly. Yet with the rise of sophisticated AI and machine learning technology, that could soon change – with enormous consequences for manufacturers and patients alike.


Case against the machine If you want to understand the future of machine learning in medical device manufacturing, you could do worse than tap Joe Corrigan. He began his career around 20 years ago, and has worked on everything from biomarkers for cardiovascular disease to imaging. And, as the current head of intelligent healthcare at Cambridge Consultants, a market leader in developing new uses of AI in the medical field, it’s now his business to keep abreast of the latest research and applications. What he has to say is worth listening to – especially when it verges on revolutionary. “The field of AI is moving forwards very quickly,” Corrigan says. This potential is certainly shadowed by the statistics. According to one report, the healthcare AI market is projected to grow to $31.3bn by 2025, at a CAGR of 41.5%, with the biggest companies habitually


securing hundreds of millions of dollars in funding from eager investors. All the same, Corrigan cautions that, until recently anyway, device manufacturers struggled to use the extraordinary potential of AI in practice. To understand what he means, think about a hypothetical manufacturing facility, making thousands of stethoscopes every hour. Most of the finished devices will be flawless – and some irredeemably damaged. But what about in those ambiguous cases, when it’s unclear if a stethoscope has a fatal scratch or a simple knit line – or the light in the facility is just unusual and that makes it hard to decide? In theory, an AI should be able to use machine learning and thousands of examples to make the right call, saving human operators hours of inspection time. Unfortunately, that’s not always been true. “The problem,” says Corrigan, “has been that training systems relied on either the expertise of the algorithm designer to identify the important features or for the training data to contain a complete set of flaws from which the algorithm can learn – but these may be very subtle, or sufficiently rare that they don’t appear in the training set.” To put it another way, there have traditionally been too many variables for AI to fully understand the vagaries of the modern production line; unlike human operators, it can’t extrapolate to spot or solve issues for which it hasn’t specifically been trained. Beyond the technology itself, meanwhile, manufacturing AI has had to deal with a host of other challenges. For one thing, it always has the potential of being overrun by hackers. Given 88% of medical technology executives admit they’re unprepared for a breach, even though


Medical Device Developments / www.nsmedicaldevices.com


asharkyu/Shutterstock.com


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