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Machine learning in radiology

What will be the impact of Artificial Intelligence on the radiology profession in the years to come? Some are excited by the prospect of computers developing the ability to recognize pathology on medical images while others fear it will take jobs from radiologists. Many of the large international radiology meetings now have sessions devoted to machine learning (ML) and new journals focusing on machine learning in radiology are being introduced. The area of ML that has created the greatest excitement and shown the most promise for medical imaging is called Deep Learning (DL). Deep Learning is based on the concept of multi-layered artificial neural networks (ANNs). An image is fed into the input layer and is passed forward through links, called weights, to a number of hidden layers that process the image to produce an output. The networks learn by a process called backpropagation that determines how the weights between layers are altered to reduce the error of the output. Backpropagation was invented in the 1960s and applied to ANNs in the 1970s. Deep neural networks (DNNs) are ANNs with many layers, hence the term Deep Learning. The many layers give the networks greater complexity for solving difficult problems. Machine learning has been around for many years but the rapid progress that we are currently witnessing in the field of ML in radiology is due to increased computer power, massive digital image databases and improvements in model architecture. It is not only the large vendors such as Siemens and GE that are developing software for radiodiagnosis. Other large companies such as Google and IBM are well positioned and actively pursuing the opportunities that ML has provided for their entry into the medical imaging arena, not to mention the many start up companies scavenging for market share. There are enormous potential benefits that we can look forward to as ML takes a more prominent role in our working life. As our individual workloads continue to rise, it will be a relief to have the more mundane studies taken from our reporting lists, giving us time for the other important tasks we currently find difficult to get time for, such as teaching, research and meeting preparation, among others. However, there is now an element of uncertainty as to what the future holds for our profession and how this new technology will affect jobs for radiologists. As a profession we own a body of knowledge and skills. As that knowledge is gradually being imparted into computer algorithms, we need to be aware that that knowledge may no longer be solely the domain of our profession, unless we, as a professional body, develop and maintain those algorithms. To that end, it is gratifying to see a number of radiologists in this country have been quick to embrace the new technology and develop expertise in this field. If we work together to develop these algorithms within our field,