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.

Additive Manufacturing of Precision Biomaterials

“…The ability to harness these data sets for the design of precision biomaterials will be critical to its successful implementation. Machine learning is already implemented in the assessment and diagnosis of high content medical images . Adapting these skills to anatomical images during surgical planning or device design will enable machine learning to assist in the optimization of the structure, materials, and print path for the AM of biomedical devices.…”

Computational models and neural nets: Fantastic models—Where to find them and how to identify them

“…Whether this concept represents an electronic obscurial more than just a black box is the subject of debate. Until now the commonest application of neural nets in medicine has been in the analysis of images as these were data rich and the most problematic for classical methods . The problem in CVD for risk prediction has been the availability of large EHR datasets.…”

“…Until now the commonest application of neural nets in medicine has been in the analysis of images as these were data rich and the most problematic for classical methods. 19 The problem in CVD for risk prediction has been the availability of large EHR datasets. This is now changing with the rapid computerisation of health systems.…”

Artificial intelligence, machine learning and deep learning: definitions and differences

“…1). 4 ML is a subset of AI, which includes all the approaches that allow machines to learn from data without being explicitly programmed. 5 The intention of ML is to train machines based on the provided data and algorithms.…”

“…DL is a subset of ML, and incorporates computational models and algorithms that imitate the architecture of the biological neural networks in brain [artificial neural networks (ANNs)]. 4,6,7 Whenever the brain receives new information, it tries to compare it with already known information to try to make sense of it. The brain deciphers the information through labelling and assigning the items to various categories, and DL employs the same concept.…”