Procurement of artificial intelligence for radiology practice

Abstract

The development of artificial intelligence (AI) technology for radiology has accelerated in the past decade, but its deployment in radiology practices has been slow. We take a sociotechnical approach and suggest that the limited use of AI in radiology practices can be attributed to a recurring tension between planned and emergent change. The paper contributes with a conceptualization and understanding of the tension during the procurement of AI for radiology. To balance this tension, we suggest that health organizations need to redefine the concept and scope of traditional procurement projects, with well-defined goals and project time. Instead, we propose that health organizations need to conceptualize their procurement and implementation projects of AI technology as evolving change processes. The study is based on an interpretive research approach and informed by the Information Infrastructure framework. Empirically, we study the procurement of AI solutions for radiology at a large health trust in Norway.

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