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Machine Learning Approaches along the Radiology Value Chain - Rethinking Value Propositions

 
: Hofmann, Peter; Oesterle, Severin; Rust, Paul; Urbach, Nils

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Twenty-Seventh European Conference on Information Systems, ECIS 2019. Proceedings : June 8-14, 2019, Stockholm-Uppsala, Sweden
AIS Electronic Library (AISeL), 2019
ISBN: 978-1-7336325-0-8
Paper 158, 19 pp.
European Conference on Information Systems (ECIS) <27, 2019, Stockholm-Uppsala>
English
Conference Paper, Electronic Publication
Fraunhofer FIT ()
artificial intelligence; machine learning; radiology; health IT; business model

Abstract
Radiology is experiencing an increased interest in machine learning with its ability to use a large amount of available data. However, it remains unclear how and to what extent machine learning will affect radiology businesses. Conducting a systematic literature review and expert interviews, we compile the opportunities and challenges of machine learning along the radiology value chain to discuss their implications for the radiology business. Machine learning can improve diagnostic quality by reducing human errors, accurately analysing large amounts of data, quantifying reports, and integrating data. Hence, it strengthens radiology businesses seeking product or service leadership. Machine learning fosters efficiency by automating accompanying activities such as generating study protocols or reports, avoiding duplicate work due to low image quality, and supporting radiologists. These efficiency improvements advance the operational excellence strategy. By providing personnel and proactive medical solutions beyond the radiology silo, machine learning supports a customer intimacy strategy. However, the opportunities face challenges that are technical (i.e., lack of data, weak labelling, and generalisation), legal (i.e., regulatory approval and privacy laws), and persuasive (i.e., radiologists resistance and patients distrust). Our findings shed light on the strategic positioning of radiology businesses, contributing to academic discourse and practical decision-making.

: http://publica.fraunhofer.de/documents/N-559057.html