AI in Healthcare: Application in Medical Imaging


Gregory Hong is an IPilogue Writer and a 1L JD candidate at Osgoode Hall Law School.


This past summer, I had the privilege, as my final act as a graduate student, to attend a major magnetic resonance imaging (MRI) conference in London, UK (ISMRM). At this conference, GE Healthcare used its plenary session to highlight their recent incorporation of AI/Deep Learning into their MRI suite. The other major MRI manufacturers, Siemens Healthineers and Philips, also have AI suites. Computed tomography (CT) had joined the AI party even earlier than MRI, with Canon, GE, Siemens and Philips products. The widespread adoption of AI in medical imaging products is significant because it is one of the first commercial applications of AI in healthcare.

What are MRI and CT?

MRI and CT are the workhorses of most hospitals’ radiology departments. CT and MRI both allow for a 3D image to be taken of internal anatomy, making them invaluable for diagnosing many diseases. Unfortunately, they both have at least one critical downside. CT is an extension of x-ray and thus exposes patients to ionizing radiation, with a CT image often depositing more than 10x the effective radiation dose of an x-ray image. MRI is lauded for, among other benefits, avoiding this radiation; however, MRI is both expensive to run and comparatively very time-consuming.

How does AI come into play?

The primary goal of AI in MRI and CT applications is mitigating the downsides – radiation dose in CT, and scan time in MRI. In both cases, this goal is achieved by “training” an AI through machine learning – or, more specifically, deep learning algorithms – by feeding it an enormous amount of data consisting of previously acquired images. Trained AI allows MRI and CT to acquire less data as the AI is used to fill in the data shortfall – almost analogous to the Hollywood idea of zooming in on a pixelated picture and seeing a clear image. Acquiring less data means less views in CT, leading to less radiation dose and shorter MRI scan times. The resulting AI-enhanced images are used for diagnostic purposes in the same way that conventionally acquired images are.

Why does it matter?

Directly related to healthcare, Canadian patients continue to suffer long waits for medical imaging, and any improvements to MRI and CT will aid in alleviating that pileup to some extent. It is also significant that radiologists and medical physicists approve of AI in diagnostic imaging. There may not be any group in the medical field more qualified to have at least some grasp of the very complex inner workings of machine learning (disclaimer: I do not fully understand the title of this thesis). It also represents one of the first applications of AI that directly affects medical decisions, which may open the door for other AI applications in healthcare. Lastly, using AI in a commercially available product is interesting on its own – the pathway toward deploying AI in such a high-stakes application may be a useful example for future AI-based products.

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