The promise of AI (part one)

The promise of AI (part one)

por Sean Ruck, Contributing Editor | August 24, 2018
Dr. Luciano Prevedello
From the August 2018 issue of HealthCare Business News magazine

Dr. Luciano Prevedello, a radiologist at the Ohio State University Wexner Medical Center (OSUWMC) and chief of the division of medical imaging informatics, appreciates the potential he sees for artificial intelligence in healthcare. He’s well-versed in the evolution of the technology, and over the last three years, he has had the opportunity to more deeply explore the strengths and weaknesses of deep learning applications. Recognizing the importance of the field, under the leadership of Dr. Richard White, chair of Radiology, the department has decided to expand activities in this area and create a lab dedicated to developing and studying AI applications in medical imaging.

In this first part of HealthCare Business News’ conversation with Dr. Prevedello, we’re taking a look at AI’s past, present and future in healthcare, as well as some of the challenges and benefits it faces in the realm of medical imaging.

Although there’s a lot of buzz about developments in AI, and Prevedello shares the excitement, there are some things he sees differently. For one, while it’s a developing story, it’s also an old story. “My view is probably different than some people,” he explained. “A lot of people refer to AI as a futuristic thing, but AI is already happening and has been here for quite some time. AI in the form of machine learning has already been used in many applications in medical imaging.”

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For Prevedello, what is different now is that recent developments in machine learning, and more specifically, deep learning, will enable more complex analyses and will broaden the scope of artificial intelligence applications in Medical Imaging. These new techniques can be applied not only to medical images but also to free text clinical reports. While some degree of automation was possible with traditional machine learning tools, they required extensive customization making development very resource intensive. “It was nearly impossible to apply the exact same algorithm to different clinical scenarios”, he explains. Recent techniques are much more generalizable. For example, the same deep learning algorithm can be trained to recognize pneumonia on a chest x-ray or intracranial hemorrhage on a CT of the head. “In the near future, we will start seeing the introduction of new algorithms doing much more sophisticated things. These advancements will not be necessarily readily apparent to us. Sometimes, it just looks as though the applications have become “smarter”. It’s similar to what we’ve seen for the web search industry. In the beginning it was simpler, string-based matching and there were some rule-based algorithms that could connect us to websites. And then Google introduced a different way of dealing with the web, with a lot more sophistication and machine learning behind the scenes,” Prevedello said.

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