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.