The augmented radiologist: artificial intelligence in the practice of radiology

Abstract

In medicine, particularly in radiology, there are great expectations in artificial intelligence (AI), which can “see” more than human radiologists in regard to, for example, tumor size, shape, morphology, texture and kinetics — thus enabling better care by earlier detection or more precise reports. Another point is that AI can handle large data sets in high-dimensional spaces. But it should not be forgotten that AI is only as good as the training samples available, which should ideally be numerous enough to cover all variants. On the other hand, the main feature of human intelligence is content knowledge and the ability to find near-optimal solutions. The purpose of this paper is to review the current complexity of radiology working places, to describe their advantages and shortcomings. Further, we give an AI overview of the different types and features as used so far. We also touch on the differences between AI and human intelligence in problem-solving. We present a new AI type, labeled “explainable AI,” which should enable a balance/cooperation between AI and human intelligence — thus bringing both worlds in compliance with legal requirements. For support of (pediatric) radiologists, we propose the creation of an AI assistant that augments radiologists and keeps their brain free for generic tasks.

Explainable multi-instance and multi-task learning for COVID-19 diagnosis and lesion segmentation in CT images

“…Furthermore, it also requires human oversight and a more complete explanation. Interactive machine learning with the ”human in the loop” could be a potential solution to this limitation of AI [42] , [43] . It should be noted that physicians/radiologists have conceptual understanding and experience that no AI can fully learn.…”

Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review

“…Additionally, in several reports, radiologists have expressed that they do not believe AI will replace them in the foreseeable future [ 14 , 18 , 19 ]. The model usually proposed is a cooperation of AI and radiologists, with AI facilitating and increasing the efficiency of radiologists and improving the diagnostic and prognostic workflows [ 20 ]. A radiologist might not need to know the technical details of ML/DL algorithms; however, knowing some general concepts would help one prepare for an even-more technology-rich practice.…”

Radiologist’s Role in Artificial Intelligence Era

“…The labeled data require radiologists to label, and its quality depends on the labeling of radiologist as such. 9 Other datasets, including validation and test sets, are fed again to validate after finishing the learning process. External validation by independent cohort and from real-life data from several institutions is advised.…”

“…This type of variability makes an unnecessary complexity for the computer to normalize data. 9 Unsupervised learning learns the dataset on the basis of data patterns without using ground truth and as in the developmental process. 13 Hybrid learning that uses partially labeled data and unlabeled data is the other future option.…”