Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine

Abstract

One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as "machine/deep learning" and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100-150 per year in 2007-2008 to 700-800 per year in 2016-2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6-9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science. Radiologists, who were on the forefront of the digital era in medicine, can guide the introduction of AI into healthcare. Yet, they will not be replaced because radiology includes communication of diagnosis, consideration of patient's values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams.

Keywords: Artificial intelligence; Deep learning; Machine learning; Neural networks (computer); Radiology.

Fig. 1

Deep learning as a subset of machine learning methods, which represent a branch of the existing artificial intelligence techniques. Machine learning techniques have been extensively applied since the 1980s. Deep learning has been applied since the 2010s due to the advancement of computational resources

Fig. 2

Comparison between classic machine learning and deep learning approaches applied to a classification task. Both depicted approaches use an artificial neural network organised in different layers (IL input layer, HL hidden layer, OL output layer). The deep learning approach avoids the design of dedicated feature extractors by using a deep neural network that represents complex features as a composition of simpler ones

Fig. 3

Graphical representation of the different relationship between the amount of data given to traditional ML or DL systems and their performance. Only DL systems continue to increase their performance

Fig. 4

Number of publications indexed on EMBASE obtained using the search query (‘artificial intelligence’/exp. OR ‘artificial intelligence’ OR ‘machine learning’/exp. OR ‘machine learning’ OR ‘deep learning’/exp. OR ‘deep learning’) AND (‘radiology’/exp. OR ‘radiology’ OR ‘diagnostic imaging’/exp. OR ‘diagnostic imaging’) AND ([english]/lim). EMBASE was accessed on April 24, 2018. For each year the number of publications was stratified for imaging modality. US ultrasound, MRI magnetic resonance imaging, CT computed tomography, PET positron emission tomography, SPECT single-photon emission tomography. Diagnostic modalities different from those listed above are grouped under the “other topic” label (e.g. optical coherence tomography, dual-energy x-ray absorptiometry, etc.)

Fig. 5

Number of publications indexed on EMBASE obtained using the search query (‘artificial intelligence’/exp. OR ‘artificial intelligence’ OR ‘machine learning’/exp. OR ‘machine learning’ OR ‘deep learning’/exp. OR ‘deep learning’) AND (‘radiology’ OR ‘diagnostic imaging’). EMBASE was accessed on April 24, 2018. For each year, the number of publications was subdivided separating opinion articles, reviews and conference abstracts from original articles in seven main subgroups considering subspecialty or body part. Other fields of medical imaging different from those listed above are grouped under the “other topics” label (e.g. dermatology, ophthalmology, head and neck, etc.)