There are many tasks AI can perform in the context of radiology. Some tasks will require just a medical image as input and will base the analysis purely on the pixels or voxels. Others will go one step further and will combine radiological images with information obtained from other sources. Combining medical images with other information can lead to insights that are not always easily to obtain for radiologists. These types of analyses are usually considered more futuristic. For example, by linking image data to pathology lab results it is possible to let an algorithm derive pathology information from a medical image. Another example is an algorithm that extracts genetic data from images without having access to genetic markups of a patient.

A different type of analysis is adding normative information. For example, by comparing patient organ volumes to the average of the population. This can be useful in dementia research (comparing volumes of specific brain structures to a normative database) or in case of splenic enlargement. Each diagnostic process aims to realize the best patient outcomes. Medical imaging is increasingly part of the diagnostic chain and should therefore be aimed at the exact same end goal: benefiting the patient.

AI offers great potential to increase quality of current image readings. For example, by performing analysis that are currently not performed because those are too time consuming for radiologists to execute manually. An example is volumetric measurements of organs, where manual delineation is too demanding time wise, but could improve the accuracy of the diagnosis. Additionally, AI is an important enabler of precision medicine. As more patient data becomes available, we can determine in a more detailed way what information implies certain treatments leading to better patient outcomes.