Women's Perspectives on Integrating Artificial Intelligence in Breast Cancer Screening Services in Abu Dhabi, United Arab Emirates

dc.contributor.author Elhadi, Yasir Ahmed Mohammed
dc.contributor.author Abdullahi, Aminu S.
dc.contributor.author Al Shamsi, Alreem
dc.contributor.author Althehli, Aysha
dc.contributor.author Alzaabi, Hamda
dc.contributor.author Al Ali, Hind
dc.contributor.author Rahma, Azhar T.
dc.date.accessioned 2026-02-15T21:44:07Z
dc.date.available 2026-02-15T21:44:07Z
dc.date.issued 2025
dc.description.abstract Artificial intelligence (AI) offers opportunities to enhance breast cancer screening by improving diagnostic accuracy and reducing radiologist workload, yet its successful adoption depends on public trust and acceptability. This cross-sectional survey of 562 Emirati women aged 18 years and older in Abu Dhabi explored knowledge, perceptions, and willingness to participate in AI-supported screening. Using a structured, culturally adapted questionnaire, descriptive statistics summarized attitudes and concerns, and logistic regression identified predictors of AI-related knowledge. Most participants (69%) believed AI could improve diagnostic accuracy, although only 11% fully trusted AI without human oversight. Human clinicians remained central to decision-making, with 86% of women preferring physician judgment in cases of conflict between AI and radiologist findings. Willingness to undergo AI-supported screening was high (74%), though concerns about false results (59%) and data misuse (36%) were prevalent. Being a health professional (aOR = 2.76, 95% CI: 1.23-6.43) and having higher knowledge of breast screening methods (aOR = 8.29, 95% CI: 3.98-18.6) were significantly associated with awareness of AI use in breast cancer screening. These findings indicate that while Emirati women show cautious support for AI in breast cancer screening, trust, cultural values, and baseline knowledge are key determinants of acceptance. Public health strategies that emphasize transparent communication, robust data protection, and education on both conventional and AI-assisted screening are essential to promote equitable and ethical integration of AI technologies into cancer control programs. en_US
dc.identifier.doi 10.1038/s41598-025-30880-y
dc.identifier.issn 2045-2322
dc.identifier.scopus 2-s2.0-105027029567
dc.identifier.uri https://doi.org/10.1038/s41598-025-30880-y
dc.identifier.uri https://hdl.handle.net/20.500.14517/8755
dc.language.iso en en_US
dc.publisher Nature Portfolio en_US
dc.relation.ispartof Scientific Reports en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Artificial Intelligence en_US
dc.subject Breast Cancer Screening en_US
dc.subject Trust in AI en_US
dc.subject UAE en_US
dc.subject Digital Health Services en_US
dc.title Women's Perspectives on Integrating Artificial Intelligence in Breast Cancer Screening Services in Abu Dhabi, United Arab Emirates en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.scopusid 57219959443
gdc.author.scopusid 60013993600
gdc.author.scopusid 60321201800
gdc.author.scopusid 60321432700
gdc.author.scopusid 60320965000
gdc.author.scopusid 60321201900
gdc.author.scopusid 14424018300
gdc.author.wosid Elhadi, Yasir/Aah-8884-2021
gdc.author.wosid Rahma, Azhar/Aaw-6753-2021
gdc.description.department Okan University en_US
gdc.description.departmenttemp [Elhadi, Yasir Ahmed Mohammed; Abdullahi, Aminu S.; Al Shamsi, Alreem; Althehli, Aysha; Alzaabi, Hamda; Al Ali, Hind; Alnaqbi, Mahra; Alkindi, Shahad; Alhashmi, Sara; Rahma, Azhar T.] United Arab Emirates Univ, Publ Hlth Inst, Coll Med & Hlth Sci, Al Ain, U Arab Emirates; [Razouk, Hola] Istanbul Okan Univ, Fac Med, Istanbul, Turkiye; [Al Ameri, Mouza] Tawam Hosp, Breast Care Ctr, Tawam Oncol Ctr, Al Ain, U Arab Emirates en_US
gdc.description.issue 1 en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q1
gdc.description.volume 16 en_US
gdc.description.woscitationindex Science Citation Index Expanded
gdc.description.wosquality Q1
gdc.identifier.pmid 41407797
gdc.identifier.wos WOS:001658031900001
gdc.index.type WoS
gdc.index.type Scopus
gdc.index.type PubMed

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