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Machine Learning approaches along the Radiology Value Chain : Rethinking Value Propositions

Title data

Hofmann, Peter ; Oesterle, Severin ; Rust, Paul ; Urbach, Nils:
Machine Learning approaches along the Radiology Value Chain : Rethinking Value Propositions.
2019
Event: 27th European Conference on Information Systems (ECIS) , 08.-14.06.2019 , Stockholm, Sweden.
(Conference item: Conference , Speech )

Official URL: Volltext

Project information

Project title:
Project's official titleProject's id
Projektgruppe WI Künstliche IntelligenzNo information

Abstract in another language

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.

Further data

Item Type: Conference item (Speech)
Refereed: No
Keywords: Artificial Intelligence; Machine Learning; Radiology; Health IT; Business Models
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Professorship Business Information Systems and strategic IT management
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Professorship Business Information Systems and strategic IT management > Professorship Business Informations Systems and strategic IT management - Univ.-Prof. Dr. Nils Urbach
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Faculties
Faculties > Faculty of Law, Business and Economics
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 02 May 2019 06:06
Last Modified: 13 Aug 2019 05:19
URI: https://eref.uni-bayreuth.de/id/eprint/48762