Machine Learning Systems in Clinics – How Mature Is the Adoption Process in Medical Diagnostics?

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dc.contributor.authorPumplun, Luisa
dc.contributor.authorFecho, Mariska
dc.contributor.authorWahl-Islam, Nihal
dc.contributor.authorBuxmann, Peter
dc.date.accessioned2020-12-24T20:18:59Z
dc.date.available2020-12-24T20:18:59Z
dc.date.issued2021-01-05
dc.description.abstractIn a world with a constantly growing and aging population, health is a precious asset. Presently, with machine learning (ML), a technological change is taking place that could provide high quality healthcare and especially, improve efficiency of medical diagnostics in clinics. However, ML needs to be deeply integrated in clinical routines which highly differs from the integration of previous health IT given the specific characteristics of ML. Since existing literature on the adoption of ML in medical diagnostics is scarce, we set up an explorative qualitative study based on a conceptual basis consisting of the technological-organizational-environmental framework (TOE) and the healthcare specific framework of non-adoption, abandonment, scale-up, spread, and sustainability (NASSS). By interviewing experts from clinics and their suppliers we were able to connect both frameworks and identify influencing factors specific to the adoption process of ML in medical diagnostics.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2021.762
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/71382
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th 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.subjectPromises and Perils of Artificial Intelligence and Machine Learning: Disruption, Adoption, Dehumanisation, Governance, Risk, and Compliance
dc.subjectadoption
dc.subjectartificial intelligence
dc.subjectdiagnostics
dc.subjecthospital
dc.subjectimplementation
dc.subjectmachine learning
dc.titleMachine Learning Systems in Clinics – How Mature Is the Adoption Process in Medical Diagnostics?
prism.startingpage6317

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Promises and Perils of Artificial Intelligence and Machine Learning: Disruption, Adoption, Dehumanisation, Governance, Risk, and Compliance