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

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dc.contributor.authorHennrich, Jasmin
dc.contributor.authorFuhrmann, Hannah
dc.contributor.authorEymann, Torsten
dc.date.accessioned2023-12-26T18:41:06Z
dc.date.available2023-12-26T18:41:06Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.428
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.otherf8e2c546-df41-4ad3-8c0c-bf91d8bd6abe
dc.identifier.urihttps://hdl.handle.net/10125/106811
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectIT Adoption, Diffusion, and Evaluation in Healthcare
dc.subjectartificial intelligence
dc.subjectattitude
dc.subjectobstacles
dc.subjectradiology
dc.titleAccelerating the Adoption of Artificial Intelligence Technologies in Radiology: A Comprehensive Overview on Current Obstacles
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractRadiology 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.
dcterms.extent10 pages
prism.startingpage3537

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IT Adoption, Diffusion, and Evaluation in Healthcare