Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach
The 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 ma...
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2025
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| author | Alsalem M.A. Alamoodi A.H. Albahri O.S. Albahri A.S. Mart�nez L. Yera R. Duhaim A.M. Sharaf I.M. |
| author2 | 57200572842 |
| author_facet | 57200572842 Alsalem M.A. Alamoodi A.H. Albahri O.S. Albahri A.S. Mart�nez L. Yera R. Duhaim A.M. Sharaf I.M. |
| author_sort | Alsalem M.A. |
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| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The 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) |
| format | Article |
| id | my.uniten.dspace-36552 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Elsevier Ltd |
| record_format | dspace |
| spelling | my.uniten.dspace-365522025-03-03T15:43:02Z Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach Alsalem M.A. Alamoodi A.H. Albahri O.S. Albahri A.S. Mart�nez L. Yera R. Duhaim A.M. Sharaf I.M. 57200572842 57205435311 57201013684 57201009814 56215039400 55902830700 58165003600 17435789800 Artificial intelligence Decision making Health care Linguistics Sensitivity analysis 2-tuple linguistic Artificial intelligent Distance-based Health care application Multi attribute decision making Q?rung orthopair fuzzy 2?tuple linguistic combinative distance-based assessment Q?rung orthopair fuzzy 2?tuple linguistic fuzzy-weighted zero-inconsistency Rank ordering Trustworthy Benchmarking The 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) Final 2025-03-03T07:43:02Z 2025-03-03T07:43:02Z 2024 Article 10.1016/j.eswa.2023.123066 2-s2.0-85182503741 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182503741&doi=10.1016%2fj.eswa.2023.123066&partnerID=40&md5=7b7614f0ec44d990e73c6ac1c46d42d2 https://irepository.uniten.edu.my/handle/123456789/36552 246 123066 All Open Access; Hybrid Gold Open Access Elsevier Ltd Scopus |
| spellingShingle | Artificial intelligence Decision making Health care Linguistics Sensitivity analysis 2-tuple linguistic Artificial intelligent Distance-based Health care application Multi attribute decision making Q?rung orthopair fuzzy 2?tuple linguistic combinative distance-based assessment Q?rung orthopair fuzzy 2?tuple linguistic fuzzy-weighted zero-inconsistency Rank ordering Trustworthy Benchmarking Alsalem M.A. Alamoodi A.H. Albahri O.S. Albahri A.S. Mart�nez L. Yera R. Duhaim A.M. Sharaf I.M. Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach |
| title | Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach |
| title_full | Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach |
| title_fullStr | Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach |
| title_full_unstemmed | Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach |
| title_short | Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach |
| title_sort | evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach |
| topic | Artificial intelligence Decision making Health care Linguistics Sensitivity analysis 2-tuple linguistic Artificial intelligent Distance-based Health care application Multi attribute decision making Q?rung orthopair fuzzy 2?tuple linguistic combinative distance-based assessment Q?rung orthopair fuzzy 2?tuple linguistic fuzzy-weighted zero-inconsistency Rank ordering Trustworthy Benchmarking |
| url_provider | http://dspace.uniten.edu.my/ |