Abstract
ObjectivesTo examine the various roles of radiologists in different steps of developing artificial intelligence (AI) applications.Materials and methodsThrough 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.ResultsWe 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).ConclusionsRadiologists 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.Key Points• Radiologists can play a wide range of roles during the development of AI applications.• Both radiologists and developers need to be open to new roles and ways of interacting during the development process.• The availability of resources, time, expertise, and trust are key factors that impact how actively radiologists play roles in the development process.
Highlights
Today, applications of artificial intelligence (AI) in radiology have become too important to ignore
We identified 9 roles that radiologists played in different steps of the development process, which we describe
Radiologists were involved in the definition of the use case, they were often absent in specifying the conceptual design of the solution, such as defining the operations, designing the interface, and determining how to integrate the application to the radiology workflow
Summary
Applications of artificial intelligence (AI) in radiology have become too important to ignore. The AI applications developed in the radiology domain are growing rapidly [1]. Besides the challenges [2], these applications increasingly enter the clinical practice and can significantly impact the radiology work [3]. In this situation, the radiologists’ role does not limit to being only the “users” of the applications. Radiologists, as domain experts who have deep insights into medical image diagnosis, need to actively participate in the development process [4]
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