Clinical engineering
Technology adoption: understanding risk
At EBME Expo, it was clear that clinical engineers will have a key role in helping to tackle the risks associated with medical technologies, as increasing intelligence and connectivity present new and evolving challenges. From safety risks associated with artificial intelligence to cyber-attacks on UK hospitals, delegates were provided with some thought-provoking insights.
The biggest ever EBME Expo took place across two days (28-29 June) this year, attracting high numbers of clinical engineers and other healthcare professionals, to discuss key issues relating to EBME (clinical engineering), operating theatres, and procurement. The event has gone from strength-to-strength and, in recent years, has moved to the larger venue of the Coventry Building Society Arena. With more expert speakers than ever, the
conference programme offered a valuable insight into how we buy, use, maintain and manage healthcare technology – in operating theatres, diagnostic centres, at the hospital bedside, in virtual wards, and in war zones. High on the agenda were the risks associated with medical technologies in an era of rapid advancement in the ‘Internet of Things’ (IoT) – from artificial intelligence (AI) to connected devices. Delegates learned how there have been significant advances in AI for imaging applications, in recent years, bringing significant opportunities in terms of diagnostics. However, there are also challenges that need to be addressed. Mark Hitchman, Managing Director of Canon Medical Systems UK, gave a candid overview of the risks and safety considerations associated with AI adoption. Canon Medical Systems UK – a manufacturer
of diagnostic imaging technologies – has been supplying AI products into the NHS for around 2-3 years and has been involved with several collaborative research projects, with universities and teaching hospitals, in the UK. The company’s Edinburgh centre currently employs 150 experts within the fields of maths, physics, photonics, genomics and biology. While AI offers significant promise within the healthcare sector, particularly in radiology, Mark warned that there are also some myths around the technology that need to be addressed, along with some important safety considerations. “I’m not here to sell you AI. I’m here to give
you some warnings and share what we have learned in the last few years. ChatGPT has got us fired up but, in diagnostics, there are risks,” he commented. The first project was to develop an AI
technology that improves image quality. The technology, called ‘Advanced intelligent Clear-IQ Engine (AiCE)’, is an MRI deep learning reconstruction technology that produces exceptionally detailed MRI images. “We have taught the AI neural network what is ‘noise’ and what is ‘good data’ in an image… We have also been delivering this AI on all our CT scanners in the last few years. It works really well, and a couple of other companies have developed [AI] technology in this space as well,” he explained. He went on to present some images, with and without AI, to demonstrate how image quality can be improved. However, he also pointed out that there are myths that healthcare providers need to be aware of when deploying AI in diagnostics – especially within imaging. “Algorithms in healthcare cannot learn ‘on
the fly’. It is very regulated – the software has to be submitted to the regulators and it is ‘frozen’ within ‘a black box’. It cannot learn ‘on the hoof’, like ChatGPT, where there is a worry that it can ‘run away’,” he explained. “The other thing we have learnt, through our experts in Edinburgh, over the last four or five years, is that algorithms – certainly in diagnostic imaging – need to see at least one hundred thousand cases to reduce the error rate, not down to absolute zero, but down to single digits of percent,” he warned. He explained that a government grant was
awarded in 2018, through Innovate UK, and Canon Medical worked with a consortium of partners and the Industrial Centre for AI Research in Digital Diagnostics (iCAIRD) on the development of AI technology. iCAIRD’s objective is to allow clinicians, health planners and industry to work together, to better inform clinical questions and ultimately solve healthcare challenges more quickly and efficiently. Collaborating with the universities of Glasgow and Aberdeen, the NHS, Queen Elizabeth
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