{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:23:46Z","timestamp":1774452226600,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T00:00:00Z","timestamp":1747785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PAPQ\u2014Qualified Publication Support Program of UFPA"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Deep learning models offer innovative solutions for cervical cancer screening in vulnerable regions such as the Brazilian Amazon. These tools are particularly relevant in areas with limited access to healthcare services, where the high prevalence of the disease severely affects riverine and indigenous populations. Artificial intelligence can overcome the limitations of traditional screening methods, providing faster and more accurate diagnoses. This enables early disease detection and reduces mortality, improving equitable access to healthcare. Furthermore, the application of these technologies complements global efforts to eliminate cervical cancer, aligning with the WHO strategies. This review emphasizes the need for model adaptation to local realities, which is essential to ensure their effectiveness in low-infrastructure areas, reinforcing their potential to reduce health disparities and expand access to quality diagnostics.<\/jats:p>","DOI":"10.3390\/computers14050202","type":"journal-article","created":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T06:31:27Z","timestamp":1747809087000},"page":"202","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Exploring Deep Learning Model Opportunities for Cervical Cancer Screening in Vulnerable Public Health Regions"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0273-6383","authenticated-orcid":false,"given":"Renan","family":"de Lima","sequence":"first","affiliation":[{"name":"Postgraduate Program in Tropical Diseases (PPGDT), Tropical Medicine Center, Federal University of Par\u00e1, Bel\u00e9m 66055-240, PA, Brazil"},{"name":"Responsible AI Committee, ABRIA\u2014Brazilian Association of Artificial Intelligence, S\u00e3o Paulo 01427-000, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6267-9966","authenticated-orcid":false,"given":"Juarez","family":"Quaresma","sequence":"additional","affiliation":[{"name":"Department of Pathology, Paulista School of Medicine, Federal University of S\u00e3o Paulo, S\u00e3o Paulo 04023-062, SP, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Novais, I.R., Coelho, C.A., Machado, H.C., Surita, F., Zeferino, L.C., and Vale, D.B. 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