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

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

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

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

An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1 : fear of replacement, knowledge, and attitude

Objectives Radiologists' perception is likely to influence the adoption of artificial intelligence (AI) into clinical practice. We investigated knowledge and attitude towards AI by radiologists and residents in Europe and beyond. Methods Between April and July 2019, a survey on fear of replacement, knowledge, and attitude towards AI was accessible to radiologists and residents. The survey was distributed through several radiological societies, author networks, and social media. Independent predictors of fear of replacement and a positive attitude towards AI were assessed using multivariable logistic regression. Results The survey was completed by 1,041 respondents from 54 mostly European countries. Most respondents were male (n = 670, 65%), median age was 38 (24-74) years, n = 142 (35%) residents, and n = 471 (45%) worked in an academic center. Basic AI-specific knowledge was associated with fear (adjusted OR 1.56, 95% CI 1.10-2.21, p = 0.01), while intermediate AI-specific knowledge (adjusted OR 0.40, 95% CI 0.20-0.80, p = 0.01) or advanced AI-specific knowledge (adjusted OR 0.43, 95% CI 0.21-0.90, p = 0.03) was inversely associated with fear. A positive attitude towards AI was observed in 48% (n = 501) and was associated with only having heard of AI, intermediate (adjusted OR 11.65, 95% CI 4.25-31.92, p < 0.001), or advanced AI-specific knowledge (adjusted OR 17.65, 95% CI 6.16-50.54, p < 0.001). Conclusions Limited AI-specific knowledge levels among radiology residents and radiologists are associated with fear, while intermediate to advanced AI-specific knowledge levels are associated with a positive attitude towards AI. Additional training may therefore improve clinical adoption

An international survey on AI in radiology in 1041 radiologists and radiology residents part 2 : expectations, hurdles to implementation, and education

Objectives Currently, hurdles to implementation of artificial intelligence (AI) in radiology are a much-debated topic but have not been investigated in the community at large. Also, controversy exists if and to what extent AI should be incorporated into radiology residency programs. Methods Between April and July 2019, an international survey took place on AI regarding its impact on the profession and training. The survey was accessible for radiologists and residents and distributed through several radiological societies. Relationships of independent variables with opinions, hurdles, and education were assessed using multivariable logistic regression. Results The survey was completed by 1041 respondents from 54 countries. A majority (n = 855, 82%) expects that AI will cause a change to the radiology field within 10 years. Most frequently, expected roles of AI in clinical practice were second reader (n = 829, 78%) and work-flow optimization (n = 802, 77%). Ethical and legal issues (n = 630, 62%) and lack of knowledge (n = 584, 57%) were mentioned most often as hurdles to implementation. Expert respondents added lack of labelled images and generalizability issues. A majority (n = 819, 79%) indicated that AI should be incorporated in residency programs, while less support for imaging informatics and AI as a subspecialty was found (n = 241, 23%). Conclusions Broad community demand exists for incorporation of AI into residency programs. Based on the results of the current study, integration of AI education seems advisable for radiology residents, including issues related to data management, ethics, and legislation