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It’s been almost 40 years since a full-body magnetic resonance imaging machine scanned a patient and generated diagnostic-quality images for the first time.
A team of medical physicists, including John Mallard, Jim Hutchinson, Bill Edelstein and Tom Redpath at the University of Aberdeen, devised the scanner and signal processing methods needed to produce an image, which led to widespread use of the MRI scanner, now a ubiquitous tool in radiology departments across the world.
MRI was a game changer in medical diagnostics because it didn’t require exposure to ionizing radiation (such as X-rays) and could generate images on multiple cross-sections of the body with superb definition of soft tissues. This allowed, for example, the direct visualization of the spinal cord for the first time.
Most people have undergone an MRI or know somebody who has done so. Along with the other tools available to radiologists, MRI has become essential to confirm the extent of disease, identify whether the patient has responded to treatment and demonstrate complications and in some cases guide intervention.
But radiology has become a victim of its own success, with an exponential rise in the number of imaging examinations requested within increasingly complex healthcare systems that serve an aging population. Demand outstrips the supply of radiographers and radiologists available to produce these scans in publicly-funded healthcare systems such as the National Health Service.
In Scotland, in particular, the number of consultant radiologists has flat lined over the past 10 years while the range and complexity of imaging methods grows with each generation of scanners. Radiologists are running to stand still, with even the most efficient departments outsourcing some of their workload to external agencies.
Meanwhile, innovators see artificial intelligence opportunities in healthcare, particularly in digital-based radiology and pathology.
Machine learning algorithms fed with large amounts of past diagnoses can generate new rules for classifying scans based on past examples. Radiomics is the approach of applying this technique to diagnostic scans.
A barrier to wider use is the lack of secure access to sensitive patient data with which to develop and test AI models. Another is the public’s lack of trust in new methods — even though computerized decision making in healthcare dates as far back as the early 1970s. Finally, there is the problem of evaluating new methods based on real-world data.
“Radiology has become a victim of its own success, with an exponential rise in the number of imaging examinations requested within increasingly complex healthcare systems that serve an aging population.”
We might ask whether we need artificial intelligence in patient care at all. But the power of these new techniques could offer huge opportunities. No matter how skilled, humans are subject to fatigue, boredom and regular interruptions, and these are when errors can occur.
Machines can work without tiring, but their ability to make intuitive decisions or rely on years of experience to recognize when an abnormality poses an urgent risk is unknown.
Even without relying on artificial intelligence for complex matters, just using it for mundane tasks such as appointment booking, allocating staff and equipment, prioritizing radiologists’ jobs or incorporating data from health care records would free up clinicians’ time for other tasks.
In the U.K., iCAIRD, the Industrial Centre for Artificial Intelligence Research in Digital Diagnostics, brings together experts from the Universities of Aberdeen, Edinburgh, Glasgow and St Andrews together with the NHS and industry partners such as Canon and Phillips in a £15 million facility based in Glasgow.
Launched last year, the project will test how well artificial intelligence algorithms compare to human expertise by providing secure access to anonymized clinical data in areas including breast cancer screening, stroke diagnosis and treatment, chest X-rays from A&E and cervical and endometrial cancer pathology.
Using an established approach for secure access to anonymized images, reports and relevant clinical data, AI researchers will develop and test their methods. iCAIRD will also create a national digital pathology database.
Cancer care typically involves multidisciplinary team meetings between clinicians from different specialisms: In the same way, iCAIRD aims to incorporate multiple artificial intelligence applications to create an AI-based virtual multidisciplinary team meeting, where knowledge from radiology and pathology can direct personalized management of cancer patients.
“Even without relying on artificial intelligence for complex matters, it could free up clinicians’ time for other tasks.”
Just as new drugs require proper evaluation before use, so too must artificial intelligence methods. We are fortunate to evaluate performance of these new algorithms with real-world data. It is clearly crucial to bring the public on this journey of evaluating AI as a potential solution.
Any new way of working is likely to come at a price — whether that is profit for the firms developing AI, just as the pharmaceutical industry profits from new drugs — or at a cost to the public in the loss of absolute patient data privacy. How to balance these and ensure good governance of AI in healthcare should be a matter for public debate, and not the role of a single sector or a handful of companies.
Ultimately, the benefits are maximized if we, as healthcare staff, patients and members of the public, determine the direction of the journey. The responsibility lies with us all.
This article first appeared on The Conversation and was republished with permission.
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