The impact of AI suggestions on radiologists' decisions: a pilot study of explainability and attitudinal priming interventions in mammography examination.

Various studies have shown that medical professionals are prone to follow the incorrect suggestions offered by algorithms, especially when they have limited inputs to interrogate and interpret such suggestions and when they have an attitude of relying on them. We examine the effect of correct and incorrect algorithmic suggestions on the diagnosis performance of radiologists when (1) they have no, partial, and extensive informational inputs for explaining the suggestions (study 1) and (2) they are primed to hold a positive, negative, ambivalent, or neutral attitude towards AI (study 2). Our analysis of 2760 decisions made by 92 radiologists conducting 15 mammography examinations shows that radiologists' diagnoses follow both incorrect and correct suggestions, despite variations in the explainability inputs and attitudinal priming interventions.

How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow? A technography study in 2021.

Objectives: How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow?

Methods: We systematically analyze 393 AI applications developed for supporting diagnostic radiology workflow. We collected qualitative and quantitative data by analyzing around 1250 pages of documents retrieved from companies' websites and legal documents. Five investigators read and interpreted collected data, extracted the features and functionalities of the AI applications, and finally entered them into an excel file for identifying the patterns.

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.

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.

How does the radiology community discuss the benefits and limitations of artificial intelligence for their work? A systematic discourse analysis.

Purpose: We aimed to systematically analyse how the radiology community discusses the concept of artificial intelligence (AI), perceives its benefits, and reflects on its limitations.

Methods: We conducted a qualitative, systematic discourse analysis on 200 social-media posts collected over a period of five months (April-August 2020).

Results: The discourse on AI is active, albeit often referring to AI as an umbrella term and lacking precision on the context (e.

Applications of artificial intelligence (AI) in diagnostic radiology: a technography study.

Objectives: Why is there a major gap between the promises of AI and its applications in the domain of diagnostic radiology? To answer this question, we systematically review and critically analyze the AI applications in the radiology domain.

Methods: We systematically analyzed these applications based on their focal modality and anatomic region as well as their stage of development, technical infrastructure, and approval.

Results: We identified 269 AI applications in the diagnostic radiology domain, offered by 99 companies.

Promises of artificial intelligence in neuroradiology: a systematic technographic review.

Purpose: To conduct a systematic review of the possibilities of artificial intelligence (AI) in neuroradiology by performing an objective, systematic assessment of available applications. To analyse the potential impacts of AI applications on the work of neuroradiologists.

Methods: We identified AI applications offered on the market during the period 2017-2019.