Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future

Abstract

Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, and is time-consuming. To improve efficiency and productivity, incorporating machine learning (ML) and deep learning (DL) algorithms could expedite the process. This article aims to review the role of artificial intelligence (AI) and its contribution to data science as well as various learning algorithms in radiology. We will analyze and explore the potential applications in image interpretation and radiological advances for AI. Furthermore, we will discuss the usage, methodology implemented, future of these concepts in radiology, and their limitations and challenges.

The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus

“…In line with the first objective of the study, we analysed the main challenges in the design and integration of Artificial Intelligence in digital radiology. The eligibility process led to the choice of 20 papers [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ], among which there are mainly reviews (15 in number) (as it could be expected considering the broad topics covered) but also 5 recent scientific articles/focus articles on very specific aspects [ 15 , 26 , 27 , 29 , 30 ].…”

“…Particular emphasis was dedicated to the challenges in the engineering and in the machine in terms of hardware, software, and impact on the workflow. These studies [ 17 , 18 , 19 ] also reported important new activities such as selecting Artificial Intelligence products and vendors; piloting vendors’ Artificial Intelligence algorithms; creating our own Artificial Intelligence algorithms; implementing, optimizing, and maintaining these algorithms.…”

“…A second topic was the revolution of the workflow [ 16 ] of the radiologist; many activities could be possible in relation to imaging and many other activities in relation to the administration of other phases of the patient management process (i.e., patient scheduling). The third topic related to the new spreading IT tools that could be strategic in the health domain [ 17 , 18 , 19 ]. This determined (and hence the fourth topic) the need to intensify the collaboration between [ 19 , 20 , 21 ] the insiders.…”

“…In subsequent studies, it will be necessary to deepen this theme based on the categorization proposed by this study. This categorizing certainly also had an impact on IT tools and training databases [ 17 , 18 , 19 , 28 ], which could be appropriately addressed with further specific studies.…”

“…The challenges in Digital Radiology were also mentioned in the study by Ahmad [17], in the study by Hamed et al [18], and in the study by Kottler [19]. Particular emphasis was dedicated to the challenges in the engineering and in the machine in terms of hardware, software, and impact on the workflow.…”