To build an algorithm you (almost) always need a dataset, the training data, to get started. This dataset will be a batch of the type of data you want your algorithm to analyze. In radiology, this would be image data. Depending on the type of algorithm used, you may need additional information as well. This may be information on what you see in the image (e.g. a segmentation) or other patient information.

Depending on the context, several definitions for artificial intelligence can be used. Many of these definitions link human behavior to the (intended) behavior of a computer. In the case of radiology these definitions do not quite cover the scope of AI as there are many situations where AI exceeds human capabilities. In radiogenomics, for example, we link genetic information to what we see on medical images, enabling us to predict the presence or absence of genetic mutations in a tumor which can be used to determine further diagnosis and management.

Machine learning algorithms are a subset of artificial intelligence methods, characterized by the fact that you do not have to tell the computer how to solve the problem in advance. Instead, the computer learns to solve tasks by recognizing patterns in the data.

Deep learning is a subset of machine learning with the main differentiating factor being that deep learning uses “deep neural networks”, whereas machine learning comprises a much broader set of techniques. Deep neural networks are similar to the simple network described previously. However, deep networks have hidden layers between the input and the output layer to refine the calculations and hence the predictions. Simple neural networks require pre-processing to derive the image features which will be the input data for the network, whereas deep neural networks can use the image directly as input.

Radiologists are extremely busy healthcare professionals. They cannot afford to make any mistakes. They need to interact with a wide range of referring physicians; neurologists, urologists, orthopedic practitioners, the list goes on. They need to be sharp, always. What AI can bring these stretched radiologists and make them even better at what they do is:

Provide a more differentiated diagnosis

Pick up repetitive routine tasks, Offer a second opinion

Eliminate inter- and intra-observer variability, Quality increase for better patient outcomes, Efficiency improvement to benefit the patient

No, it will not take over radiologist jobs. It most certainly will take over some radiologist tasks. It will support radiologists by performing automatic measurements which are currently very time consuming. It will pick up routine tasks which are experienced as cumbersome by a lot of radiologists. Yet, radiologists have a much more differentiated job than these type of tasks alone. In summary, AI is here to assist radiologist to make their lives easier.