Although Artificial Intelligence (AI) Computer Aided Diagnosis (CAD) frameworks can detect and classify lesions in mammograms more accurately than radiologists, the adoption rate of such frameworks is low. To address that lacuna, in this research project we explore the drivers for the adoption of AI-CAD frameworks as perceived by companies that develop medical imaging algorithmic solutions, the radiologists that use such solutions, and patients that will ultimately benefit from them. The preliminary findings of our research project we present here from interviews with experts in the medical imaging industry, suggest that there are four main drivers for the adoption of AI-CAD, namely: i) product excellence, ii) integration with the hospital IT and clinician’s workflow, iii) stakeholder acceptance, as well as iv) legal and ethical aspects. We discuss the implications of our preliminary findings for both theory, and practice and delineate an agenda for future research on the topic.
Gante, Stefanie and Angelopoulos, Spyros, "Paving the way toward Human-Algorithm Interactions: Understanding AI-CAD adoption for breast cancer detection" (2022). ECIS 2022 Research-in-Progress Papers. 29.
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