The Impact of Information Technology on Radiology Services: An Overview

The main objective of this paper is to provide an overview of the impact of information technology on radiology services during the past 15 years and to promote awareness of the digital revolution that is taking place in health care, including radiology. The combination of two major innovations is playing a central role in this revolution, namely, the Internet and the digitisation of medical information. The various stages of the Internet development and their relationship with the almost simultaneously ongoing digitisation of the radiology department are described. The onset of teleradiology services and the more recent trend toward the usage of cloud-based networks and services are explained. The recent changes in digital communication and electronic transmission of medical information are discussed, hereby paying attention to the value of social media in medicine and radiology in particular. Finally, the future prospects of health care and medical imaging are outlined in the spotlight of today’s major trends, and the role of the radiologist in this quickly changing environment is redefined

Challenges and solutions for introducing artificial intelligence (AI) in daily clinical workflow

Objectives!#!The aim of the study was to evaluate the effect of bolus-tracking ROI positioning on coronary computed tomography angiography (CCTA) image quality.!##!Methods!#!In this retrospective monocentric study, all patients had undergone CCTA by step-and-shoot mode to rule out coronary artery disease within a cohort at intermediate risk. Two groups were formed, depending on ROI positioning (left atrium (LA) or ascending aorta (AA)). Each group contained 96 patients. To select pairs of patients, propensity score matching was used. Image quality with regard to coronary arteries as well as pulmonary arteries was evaluated using quantitative and qualitative scores.!##!Results!#!In terms of the coronary arteries, there was no significant difference between both groups using quantitative (SNR AA 14.92 vs. 15.46; p = 0.619 | SNR LM 19.80 vs. 20.30; p = 0.661 | SNR RCA 24.34 vs. 24.30; p = 0.767) or qualitative scores (4.25 vs. 4.29; p = 0.672), respectively. With regard to pulmonary arteries, we found significantly higher quantitative (SNR RPA 8.70 vs. 5.89; p < 0.001 | SNR LPA 9.06 vs. 6.25; p < 0.001) and qualitative scores (3.97 vs. 2.24; p < 0.001) for ROI positioning in the LA than for ROI positioning in the AA.!##!Conclusions!#!ROI positioning in the LA or the AA results in comparable image quality of CT coronary arteriography, while positioning in the LA leads to significantly higher image quality of the pulmonary arteries. These results support ROI positioning in the LA, which also facilitates triple-rule-out CT scanning.!##!Key points!#!• ROI positioning in the left atrium or the ascending aorta leads to comparable image quality of the coronary arteries. • ROI positioning in the left atrium results in significantly higher image quality of the pulmonary arteries. • ROI positioning in the left atrium is feasible to perform triple-rule-out CTA

Introduction to structured reporting

Structured reporting has substantial interest in radiology. As this way of reporting increases quality and allows for structural availability of report data elements, it is of strategic interest for radiology to increase this way of reporting. Artificial intelligence, decision support, and natural language processing could help to facilitate the acceptance of structured reporting in routine clinical practice

Sharing Imaging Data

In the past decades, the abilities to share imaging data quickly developed. In a short period of time, radiology went from a film-based operation with very limited image sharing to a fully digital environment where all imaging data can be shared within the institute, between institutes, and with patients. During such a change process, careful considerations have to be made when new technical developments are introduced in each step in the digital transition. Development and implementation of standards and guidelines helped the transition to continue resulting in a high level of interoperability for medical imaging we can observe today. Most recently, the widespread adoption of Social Media required radiology to again re-think the way images are shared and communicated. In this chapter, we provide an overview of the changes in the way imaging data are shared, and discuss the possible pitfalls and challenges that have to be considered

Radiologists in the loop:the roles of radiologists in the development of AI applications

Objectives To examine the various roles of radiologists in different steps of developing artificial intelligence (AI) applications. Materials and methods Through the case study of eight companies active in developing AI applications for radiology, in different regions (Europe, Asia, and North America), we conducted 17 semi-structured interviews and collected data from documents. Based on systematic thematic analysis, we identified various roles of radiologists. We describe how each role happens across the companies and what factors impact how and when these roles emerge. Results We identified 9 roles that radiologists play in different steps of developing AI applications: (1) problem finder (in 4 companies); (2) problem shaper (in 3 companies); (3) problem dominator (in 1 company); (4) data researcher (in 2 companies); (5) data labeler (in 3 companies); (6) data quality controller (in 2 companies); (7) algorithm shaper (in 3 companies); (8) algorithm tester (in 6 companies); and (9) AI researcher (in 1 company). Conclusions Radiologists can play a wide range of roles in the development of AI applications. How actively they are engaged and the way they are interacting with the development teams significantly vary across the cases. Radiologists need to become proactive in engaging in the development process and embrace new roles

Training opportunities of artificial intelligence (AI) in radiology:a systematic review

Objectives The aim is to offer an overview of the existing training programs and critically examine them and suggest avenues for further development of AI training programs for radiologists. Methods Deductive thematic analysis of 100 training programs offered in 2019 and 2020 (until June 30). We analyze the public data about the training programs based on their “contents,” “target audience,” “instructors and offering agents,” and “legitimization strategies.” Results There are many AI training programs offered to radiologists, yet most of them (80%) are short, stand-alone sessions, which are not part of a longer-term learning trajectory. The training programs mainly (around 85%) focus on the basic concepts of AI and are offered in passive mode. Professional institutions and commercial companies are active in offering the programs (91%), though academic institutes are limitedly involved. Conclusions There is a need to further develop systematic training programs that are pedagogically integrated into radiology curriculum. Future training programs need to further focus on learning how to work with AI at work and be further specialized and customized to the contexts of radiology wor