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

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' Usage of Social Media:Results of the RANSOM Survey

The growing use of social media is transforming the way health care professionals (HCPs) are communicating. In this changing environment, it could be useful to outline the usage of social media by radiologists in all its facets and on an international level. The main objective of the RANSOM survey was to investigate how radiologists are using social media and what is their attitude towards them. The second goal was to discern differences in tendencies among American and European radiologists. An international survey was launched on SurveyMonkey (https://www.surveymonkey.com) asking questions about the platforms they prefer, about the advantages, disadvantages, and risks, and about the main incentives and barriers to use social media. A total of 477 radiologists participated in the survey, of which 277 from Europe and 127 from North America. The results show that 85 % of all survey participants are using social media, mostly for a mixture of private and professional reasons. Facebook is the most popular platform for general purposes, whereas LinkedIn and Twitter are more popular for professional usage. The most important reason for not using social media is an unwillingness to mix private and professional matters. Eighty-two percent of all participants are aware of the educational opportunities offered by social media. The survey results underline the need to increase radiologists' skills in using social media efficiently and safely. There is also a need to create clear guidelines regarding the online and social media presence of radiologists to maximize the potential benefits of engaging with social media

Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors

Objective The objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands. Materials and methods Using an embedded multiple case study, an exploratory, qualitative research design was followed. Data collection consisted of 24 semi-structured interviews from seven Dutch hospitals. The analysis of barriers and facilitators was guided by the recently published Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework for new medical technologies in healthcare organizations. Results Among the most important facilitating factors for implementation were the following: (i) pressure for cost containment in the Dutch healthcare system, (ii) high expectations of AI’s potential added value, (iii) presence of hospital-wide innovation strategies, and (iv) presence of a “local champion.” Among the most prominent hindering factors were the following: (i) inconsistent technical performance of AI applications, (ii) unstructured implementation processes, (iii) uncertain added value for clinical practice of AI applications, and (iv) large variance in acceptance and trust of direct (the radiologists) and indirect (the referring clinicians) adopters. Conclusion In order for AI applications to contribute to the improvement of the quality and efficiency of clinical radiology, implementation processes need to be carried out in a structured manner, thereby providing evidence on the clinical added value of AI applications

Social media for radiologists: an introduction

Social media, which can be defined as dynamic and interactive online communication forums, are becoming increasingly popular, not only for the general public but also for radiologists. In addition to assisting radiologists in finding useful profession-related information and interactive educational material in all kinds of formats, they can also contribute towards improving communication with peers, clinicians, and patients. The growing use of social networking in healthcare also has an impact on the visibility and engagement of radiologists in the online virtual community. Although many radiologists are already using social media, a large number of our colleagues are still unaware of the wide spectrum of useful information and interaction available via social media and of the added value these platforms can bring to daily practice. For many, the risk of mixing professional and private data by using social media creates a feeling of insecurity, which still keeps radiologists from using them. In this overview we aim to provide information on the potential benefits, challenges, and inherent risks of social media for radiologists. We will provide a summary of the different types of social media that can be of value for radiologists, including useful tips on how to use them safely and efficiently

Artificial Intelligence Tool for Detection and Worklist Prioritization Reduces Time to Diagnosis of Incidental Pulmonary Embolism at CT

Purpose: To evaluate the diagnostic efficacy of artificial intelligence (AI) software in detecting incidental pulmonary embolism (IPE) at CT and shorten the time to diagnosis with use of radiologist reading worklist prioritization. Materials and Methods: In this study with historical controls and prospective evaluation, regulatory-cleared AI software was evaluated to prioritize IPE on routine chest CT scans with intravenous contrast agent in adult oncology patients. Diagnostic accuracy metrics were calculated, and temporal end points, including detection and notification times (DNTs), were assessed during three time periods (April 2019 to September 2020): routine workflow without AI, human triage without AI, and worklist prioritization with AI. Results: In total, 11 736 CT scans in 6447 oncology patients (mean age, 63 years ± 12 [SD]; 3367 men) were included. Prevalence of IPE was 1.3% (51 of 3837 scans), 1.4% (54 of 3920 scans), and 1.0% (38 of 3979 scans) for the respective time periods. The AI software detected 131 true-positive, 12 false-negative, 31 false-positive, and 11 559 true-negative results, achieving 91.6% sensitivity, 99.7% specificity, 99.9% negative predictive value, and 80.9% positive predictive value. During prospective evaluation, AI-based worklist prioritization reduced the median DNT for IPE-positive examinations to 87 minutes (vs routine workflow of 7714 minutes and human triage of 4973 minutes). Radiologists’ missed rate of IPE was significantly reduced from 44.8% (47 of 105 scans) without AI to 2.6% (one of 38 scans) when assisted by the AI tool (P < .001). Conclusion: AI-assisted workflow prioritization of IPE on routine CT scans in oncology patients showed high diagnostic accuracy and significantly shortened the time to diagnosis in a setting with a backlog of examinations. Supplemental material is available for this article

Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors

Objective The objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands. Materials and methods Using an embedded multiple case study, an exploratory, qualitative research design was followed. Data collection consisted of 24 semi-structured interviews from seven Dutch hospitals. The analysis of barriers and facilitators was guided by the recently published Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework for new medical technologies in healthcare organizations. Results Among the most important facilitating factors for implementation were the following: (i) pressure for cost containment in the Dutch healthcare system, (ii) high expectations of AI’s potential added value, (iii) presence of hospital-wide innovation strategies, and (iv) presence of a “local champion.” Among the most prominent hindering factors were the following: (i) inconsistent technical performance of AI applications, (ii) unstructured implementation processes, (iii) uncertain added value for clinical practice of AI applications, and (iv) large variance in acceptance and trust of direct (the radiologists) and indirect (the referring clinicians) adopters. Conclusion In order for AI applications to contribute to the improvement of the quality and efficiency of clinical radiology, implementation processes need to be carried out in a structured manner, thereby providing evidence on the clinical added value of AI applications

A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative.

BackgroundRecently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19).ObjectivesTo develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity.MethodsThe Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected.ResultsA total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user.ConclusionWe developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans

Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement

Abstract This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence, and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI which promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes