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Medical imaging AI improves speed, accuracy of diagnoses

Innovations in AI for medical imaging make it possible to diagnose conditions faster and more accurately than clinicians, who can use AI as an assistive or predictive tool.

Over the years, physicians have witnessed an evolution in medical imaging to assist in diagnosing and treating patients. Since the accidental discovery of X-rays in 1895, imaging has been used to evaluate injuries. With continued innovations in medical imaging, more advanced methods were introduced to scan the human body, and physicians are now able to take advantage of imaging from MRI, CT and nuclear scans. Historically, these technologies relied on the human eye to analyze and identify whether a patient needed treatment or not. However, that's about to change thanks to the advancements in medical imaging AI.

Recent advancements in computer vision software and powerful machine learning capabilities are enabling computer systems to perform image analysis that was once only performed by professionally trained radiologists and physicians. Robots or software algorithms are beginning to take on some of the human tasks of analyzing and reviewing medical images.  

It is not too hard to imagine the different use cases where a computer algorithm can mimic the tasks that a healthcare professional performs. For example, a dermatologist frequently inspects spots on a patient's skin visually to determine whether or not the patient has a condition or requires further testing. This routine task can be performed by AI since the technology itself is capable of analyzing images of the skin and comparing them to thousands of other images it learned from to determine what the likely issue is or what the next steps should be.

When physicians evaluate medical images, they are visually inspecting and attempting to identify abnormalities and similarities to other cases. As a result, many AI vendors saw the opportunity to take the same steps to teach applications about clinical conditions based on medical imaging data sets. A medical imaging AI platform could then use that knowledge to statistically predict the condition presented in new images it processes or scans.

By replicating the processes used by clinicians, data scientists were able to deliver a new set of software applications that can be used side by side with the radiologist, pathologist or physician to identify conditions just by analyzing the medical image. With tools in the marketplace being marketed to uncover liver, breast, cardiovascular and bone disease, these products are bound to become more popular.

It is undeniable that medical imaging AI can outperform humans in certain tasks due to its speed and knowledge. With its vision capabilities, AI can learn from medical imaging that was previously done and further analyze images for things that the human eye may not have been able to detect. This process allows medical imaging AI to not only learn to associate certain abnormalities seen in images to diagnosis, but also identify other abnormalities humans might have missed. 

AI has also shown its ability to process more data at a faster rate, improving speed and productivity. Arterys, the first company approved by the FDA for the use of deep learning capabilities in clinical applications, has shown that its product can use AI to diagnose heart problems in just 15 seconds. A human would take an average of 30 minutes to an hour to produce the same result.

Vendors that offer vision services and use medical imaging AI for its analysis and prediction capabilities are piquing the interest of many hospitals and healthcare organizations. By assisting clinicians and outperforming humans in some of the tasks, medical imaging AI is poised to see higher adoption rates and address healthcare disparities that result from resource shortages and limited coverage. With so many new startups in the AI space, healthcare groups are likely to wait on the FDA stamp of approval to ensure that new vendors meet regulatory requirements and are vetted adequately to ensure patient safety.

This was last published in August 2018

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