Use Cases of AI for Magnetic resonance imaging

The role of artificial intelligence in healthcare has been a hot topic for decades. Using MRI technology to scan organs, the human body and even functions within cells, is drawing the interest of doctors and scientists across the globe.

However, MRI scans are time-consuming, costly and come with long waiting lists. By utilising deep learning algorithms, doctors and scientists hope that with machine learning, we can reduce waiting times, cut costs and aid early diagnoses of ailments.

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Those are compelling benefits for both patients and hospitals. But just how can AI be applied to this modern healthcare marvel?


First off, it’s important to look at how the technology works. Magnetic Resonance Imaging (MRI) uses extremely strong magnetic fields and radiofrequency waves to measure the molecular activity of a particular area. Absence of signal in an area indicates an image of low intensity, which can be used to measure brain activity in schizophrenia or differentiate between tumours and healthy tissue.

The AI Read feature of artificial intelligence is especially useful because it makes sense of complex data in minutes compared to hours or days. This allows physicians to make more informed decisions about treating their patients faster than ever before.

However, AI is only as good as the data that’s fed into it, so it’s important to note that scientists are careful to heavily critique data before inputting it into AI. To ensure accuracy, measurements themselves are multiple measurements taken after a different time interval. This process called ‘k-space imaging” helps eliminate any error caused by background noise or cross talk.

Another great use case for AI is allowing radiologists to take a pause from everyday work tasks. With AI able to scan an MRI in just minutes, radiologists are able to focus on more challenging problems and complicated cases. This system can be especially helpful during a flu epidemic or in rural areas where there is a smaller workforce and more patients. It can also be used for more simple cases such as reading MRIs for further insight into the patient's health.

Meanwhile, there are still problems with having AI take over the MRI imaging process such as emotions and ethics. Because MRI images often depict sensitive parts of the body, getting traditional human radiologists involved with the AI process is important. Doctors can use AI’s technological muscle, while using their own instincts to cut down risks and maintain privacy for their patients.

So, with advances in artificial intelligence, what more can we expect to see in our medical future? What other technologies will fuel our quest for a healthier future? Make sure to check out our article about how to pick stocks before tuning back in next week!

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