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Accelerating the Adoption of Artificial Intelligence Technologies in Radiology : A Comprehensive Overview on Current Obstacles

Title data

Hennrich, Jasmin ; Fuhrmann, Hannah ; Eymann, Torsten:
Accelerating the Adoption of Artificial Intelligence Technologies in Radiology : A Comprehensive Overview on Current Obstacles.
In: Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS). - Honolulu, Hawaii , 2024

Official URL: Volltext

Project information

Project title:
Project's official title
Project's id
Projektgruppe WI Digital Society
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Projektgruppe WI Künstliche Intelligenz
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Abstract in another language

Radiology has always been considered a highly technological field in medicine. Recently, a new area of radiology has emerged with the adoption of Artificial Intelligence (AI)-based health information systems due to advancements in big data, deep learning, and increased computing power. While AI elevates prevention, diagnostics, and therapy to a new level, various obstacles hinder the adoption of AI technologies in radiology. To provide an overview on these obstacles as basis for corresponding solution approaches, we identify and comprehensively outline these obstacles by conducting a structured literature review. We find 17 obstacles, which we group into six categories. Furthermore, our research discusses relevant interrelations of the obstacles, most of which we have found to be related to user attitude. Besides, these complex interrelations we expose the necessity of approaching the obstacles simultaneously.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: Artificial Intelligence; Attitude; Radiology; Obstacles
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 > Chair Business Administration VII - Information Systems Management and Digital Society
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration VII - Information Systems Management and Digital Society > Chair Business Administration VII - Information Systems Management and Digital Society - Univ.-Prof. Dr. Torsten Eymann
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Branch Business and Information Systems Engineering of Fraunhofer FIT
Research Institutions > Affiliated Institutes > FIM Research Center for Information Management
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: 04 Oct 2023 07:06
Last Modified: 04 Oct 2023 07:06
URI: https://eref.uni-bayreuth.de/id/eprint/87027