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Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach

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dc.citedby19
dc.contributor.authorAlsalem M.A.en_US
dc.contributor.authorAlamoodi A.H.en_US
dc.contributor.authorAlbahri O.S.en_US
dc.contributor.authorAlbahri A.S.en_US
dc.contributor.authorMart�nez L.en_US
dc.contributor.authorYera R.en_US
dc.contributor.authorDuhaim A.M.en_US
dc.contributor.authorSharaf I.M.en_US
dc.contributor.authorid57200572842en_US
dc.contributor.authorid57205435311en_US
dc.contributor.authorid57201013684en_US
dc.contributor.authorid57201009814en_US
dc.contributor.authorid56215039400en_US
dc.contributor.authorid55902830700en_US
dc.contributor.authorid58165003600en_US
dc.contributor.authorid17435789800en_US
dc.date.accessioned2025-03-03T07:43:02Z
dc.date.available2025-03-03T07:43:02Z
dc.date.issued2024
dc.description.abstractThe purpose of this paper is to propose a novel hybrid framework for evaluating and benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi-criteria decision-making (MCDM) techniques under a new fuzzy environment. To develop such a framework, a new decision matrix has been built, and then integrated with q-ROF2TL-FWZIC (q?Rung Orthopair Fuzzy 2?Tuple Linguistic Fuzzy-Weighted Zero-Inconsistency) and q-ROF2TL-CODAS (q?Rung Orthopair Fuzzy 2?Tuple Linguistic Combinative Distance-Based Assessment). In this integration, q-ROF2TL-FWZIC is utilized for assigning the weights of evaluation attributes of trustworthy AI, while q-ROF2TL-CODAS is employed for benchmarking trustworthy AI applications. Findings show that the q-ROF2TL-FWZIC method effectively weights the evaluation attributes. The transparency attribute receives the highest importance weight (0.173566825), whereas the human agency and oversight criterion has the lowest weight (0.105741901). The remaining attributes are distributed in between. Moreover, alternative_4 receives the highest rank order (score of 7.370410417), while alternative_13 receives the lowest rank order (score of ?4.759794397). To evaluate the validity of the proposed framework, systematic ranking and sensitivity analysis assessments were employed. ? 2023 The Author(s)en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo123066
dc.identifier.doi10.1016/j.eswa.2023.123066
dc.identifier.scopus2-s2.0-85182503741
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85182503741&doi=10.1016%2fj.eswa.2023.123066&partnerID=40&md5=7b7614f0ec44d990e73c6ac1c46d42d2
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36552
dc.identifier.volume246
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access; Hybrid Gold Open Access
dc.sourceScopus
dc.sourcetitleExpert Systems with Applications
dc.subjectArtificial intelligence
dc.subjectDecision making
dc.subjectHealth care
dc.subjectLinguistics
dc.subjectSensitivity analysis
dc.subject2-tuple linguistic
dc.subjectArtificial intelligent
dc.subjectDistance-based
dc.subjectHealth care application
dc.subjectMulti attribute decision making
dc.subjectQ?rung orthopair fuzzy 2?tuple linguistic combinative distance-based assessment
dc.subjectQ?rung orthopair fuzzy 2?tuple linguistic fuzzy-weighted zero-inconsistency
dc.subjectRank ordering
dc.subjectTrustworthy
dc.subjectBenchmarking
dc.titleEvaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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