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Yet, our understanding of trust models is constrained, and a standard accepted approach to evaluating trust in AI models is still lacking. We introduce a novel methodology to assess and quantify HCPs\u2019 perceived trust in an interpretable machine learning model that serves as clinical decision support for diagnosing COVID-19 cases. Our approach leverages fuzzy cognitive maps (FCMs) to elicit and quantify HCPs\u2019 trust mental models for understanding trust dynamics in clinical diagnosis. Our study reveals that HCPs rely predominantly on their own expertise when interacting with the developed interpretable clinical decision support. Although the model\u2019s interpretations offer limited assistance in diagnostic tasks, they facilitate the HCPs\u2019 utilization of it. However, the impact of these interpretations on the establishment of perceived trust varies among HCPs, which can lead to an increase in trust for some while decreasing it for others. To validate quantified perceived trust, we employ the degree of agreement metric, which quantitatively assesses whether HCPs lean more towards their own expertise or rely on the model\u2019s recommendations in diagnostic tasks. We found significant alignment between the conclusions of the two metrics, indicating successful modeling and quantification of perceived trust. Plus, a moderate to strong positive correlation between the two metrics confirmed this conclusion. This means that FCMs can quantify HCPs\u2019 perceived trust, aligning with their actual diagnostic advice shift after interacting with the model.<\/jats:p>","DOI":"10.1007\/978-3-032-08327-2_10","type":"book-chapter","created":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:07:01Z","timestamp":1760206021000},"page":"202-222","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing and\u00a0Quantifying Perceived Trust in\u00a0Interpretable Clinical Decision Support"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7596-378X","authenticated-orcid":false,"given":"Mohsen","family":"Abbaspour Onari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8035-2887","authenticated-orcid":false,"given":"Isel","family":"Grau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9811-1881","authenticated-orcid":false,"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7692-7203","authenticated-orcid":false,"given":"Marco S.","family":"Nobile","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5073-0787","authenticated-orcid":false,"given":"Yingqian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,12]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Abbaspour\u00a0Onari, M., Jahangoshai\u00a0Rezaee, M.: Implementing bargaining game-based fuzzy cognitive map and mixed-motive games for group decisions in the healthcare supplier selection. 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