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Acceptance of AI-based diagnostic tools in neuroimaging

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dc.contributor Aalto University en
dc.contributor Aalto-yliopisto fi
dc.contributor.advisor Penttinen, Esko
dc.contributor.author Nenonen, Iida
dc.date.accessioned 2022-09-11T16:09:03Z
dc.date.available 2022-09-11T16:09:03Z
dc.date.issued 2022
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/116703
dc.description.abstract Artificially intelligent (AI) methods are employed in many areas of medical sciences, while they are still under-utilized in functional neuroimaging methods like magnetoencephalography (MEG). This study was performed to obtain prerequisite information of the acceptance and potential adoption of AI-based analysis methods among prominent MEG clinicians and research scientists. The study was conducted as a technology case study focusing on MEGIN Oy (Espoo, Finland) who is the global leader for MEG technology. In the future products, AI-based methods and cloud computing are strategically important for widening the clinical applications of MEG in larger patient populations and expanding the MEG market. Semi-structured interviews were conducted with MEG users in three different hospitals in the US and three in Europe. The interviews directly probed the opinions and perspectives of the clinicians and researchers of the AI methods on MEG. The study utilized the Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology frameworks, both to formulate interview questions and to account for the factors that emerged from the interview data. All interviewees showed very positive attitude towards automated and AI-based data processing methods in MEG. They also want to widen the applicability of MEG in larger patient populations. Time-efficiency of AI methods was considered the biggest advantage, along with multi-dimensionality of the interpretation. Therefore, the AI tools could advance learning of the development phases of brain disorders. Opinions on the transparency of algorithms varied, but all interviewees agreed that the validation process should have maximal transparency. New AI tools should be developed considering multiple empirical evidence and AI model training with data specific to the brain disorders. The clinical experts need to know what data is put in the tool, what data has been used in the tool validation, and how the tool’s accuracy and reliability has been proven. They also want visualization methods to assess the quality of the data and the results. Besides user acceptance, factors were discussed that in general explain the lagging adoption of AI. They include limited access to patient data, questions of data security and ownership, and regulatory barriers. en
dc.format.extent 36+14
dc.language.iso en en
dc.title Acceptance of AI-based diagnostic tools in neuroimaging en
dc.title Tekoälypohjaisten diagnoosimenetelmien hyväksyminen neurokuvantamisessa sv
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Kauppakorkeakoulu fi
dc.contributor.school School of Business en
dc.contributor.department Tieto- ja palvelujohtamisen laitos fi
dc.subject.keyword neuroimaging en
dc.subject.keyword artificial intelligence en
dc.subject.keyword technology acceptance model en
dc.subject.keyword AI diagnosis methods en
dc.identifier.urn URN:NBN:fi:aalto-202209115507
dc.type.ontasot Master's thesis en
dc.type.ontasot Maisterin opinnäyte fi
dc.programme Information and Service Management (ISM) en
dc.location P1 I fi
local.aalto.electroniconly yes
local.aalto.openaccess no

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