Recent advances in artificial intelligence have led to speculation that AI might one day replace human radiologists. Researchers have developed deep learning neural networks that can identify pathologies in radiological images such as bone fractures and potentially cancerous lesions, in some cases more reliably than an average radiologist. For the most part, though, the best systems are currently on par with human performance and are used only in research settings. That said, deep learning is rapidly advancing, and it's a much better technology than previous approaches to medical image analysis. This probably does portend a future in which AI plays an important role in radiology.
Artificial intelligence (AI) has captured the imagination and attention of doctors over the past couple years as several companies and large research hospitals work to perfect these systems for clinical use. The first concrete examples of how AI (also called deep learning, machine learning or artificial neural networks) will help clinicians are now being commercialized. These systems may offer a paradigm shift in how clinicians work in an effort to significantly boost workflow efficiency, while at the same time improving care and patient throughput. Today, one of the biggest problems facing physicians and clinicians in general is the overload of too much patient information to sift through. This rapid accumulation of electronic data is thanks to the advent of electronic medical records (EMRs) and the capture of all sorts of data about a patient that was not previously recorded, or at least not easily data mined.
At RSNA 2016, the majority of significant new product announcements were modalities, not information technology. It almost seems that many radiology IT companies (or business segments) are planning to release new product introductions at HIMSS rather than at RSNA. While enterprise imaging remains the core radiology IT technology on display at RSNA, the big buzz this year was artificial intelligence and machine learning. As part of their Opening Session, Keith J. Dreyer, DO, PhD, and Robert M. Wachter, MD, discussed the good and the bad of the digital revolution in radiology. With artificial intelligence (AI) rapidly advancing thanks to events such as the ImageNet Large Scale Visual Recognition Challenge Competition, Dr. Dreyer believes AI will complement radiology and enable radiologists to become leaders in precision medicine; rather than becoming wary of AI, he said, radiology could work with AI to optimize the delivery of patient care.
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.