Today I spent the majority of my time speaking to a prestigious world renown radiologist. He happens to be dual board certified, fellowship trained, has multiple decades of experience in reading images, curating information that makes up a patient's medical history, has an amazing "sense" for what is relevant versus not relevant, and is a great teacher. How do I begin with a binary action and then segment into various branches that can get a model started in learning how to "see" as a radiologist may interpret an image. After 9 hours in a room, multiple sticky notes, butcher paper taped all over the room, several sharpies, and a few diet cokes I am happy to say that we began to create a plan to at least begin the journey. It is always fascinating to me to learn from such amazing physicians and then translate their knowledge into the language of someone, or in this case, something else so that it can be understood.
Whatever its name, it's the same thing – machines recognizing clinical problems in digital images ahead of the radiologists charged with making the diagnosis. The artificial intelligence (AI) trend is new, but it's gaining ground quickly, according to industry experts. The advent of these technologies and radiology's growing interest in and dependence on them has been discussed at national and international meetings, including the RSNA, HIMSS, and SIIM annual meetings, during the past year. But, there's still a long way to go. "We're just barely scratching the surface of using artificial intelligence in the last few years," said Eliot Siegel, MD, professor and vice chair of research information systems for the University of Maryland Department of Diagnostic Radiology and Nuclear Medicine.
Algorithms based on machine learning and deep learning, intended for use in diagnostic imaging, are moving into the commercial pipeline. However, providers will have to overcome multiple challenges to incorporate these tools into daily clinical workflows in radiology. There now are numerous algorithms in various stages of development and in the FDA approval process, and experts believe that there could eventually be hundreds or even thousands of AI-based apps to improve the quality and efficiency of radiology. The emerging applications based on machine learning and deep learning primarily involve algorithms to automate such processes in radiology as detecting abnormal structures in images, such as cancerous lesions and nodules. The technology can be used on a variety of modalities, such as CT scans and X-rays.
The same artificial intelligence that may soon drive your new car is being adapted to help drive interventional radiology care for patients. Researchers at the University of California, Los Angeles (UCLA), have used advanced artificial intelligence, also called machine learning, to create a "chatbot" or Virtual Interventional Radiologist (VIR). This device communicates automatically with a patient's physicians and can quickly offer evidence-based answers to frequently asked questions. The scientists will present their research today at the Society of Interventional Radiology's 2017 annual scientific meeting in Washington, D.C. This breakthrough will allow clinicians to give patients real-time information on interventional radiology procedures as well as planning the next step of their treatment.
Futurists sometimes claim that artificial intelligence (AI) will make radiologists obsolete. Their argument has been that compared to humans, algorithms are better and faster at analyzing medical images such as X-rays. So why has this hype failed to become reality? In this opinion piece, Ulysses Isidro and Saurabh Jha write, "For radiology AI to be widely adopted, it needs to overcome several barriers. Most of all, it needs to make the radiologist's work simpler."