{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T20:59:23Z","timestamp":1790369963060,"version":"4.1.0"},"reference-count":144,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Women\u2019s reproductive and endocrine disorders including Polycystic Ovary Syndrome (PCOS), endometriosis, thyroid disorders, infertility, and pregnancy-related complications remain a major global health burden. These conditions are especially difficult to manage in low- and middle-income countries (LMICs), where diagnostic facilities and specialist care are limited. We conducted a PRISMA-ScR\u2013guided scoping review of artificial intelligence (AI) and machine learning (ML) applications in women\u2019s reproductive and hormonal health. Our search covered five databases PubMed, Scopus, IEEE Xplore, Web of Science, and Google Scholar and included peer-reviewed studies published between 2021 and 2025. A total of 116 eligible studies were identified and classified into six categories: clinical prediction and risk modeling, medical imaging and AI, biomarker discovery and multi-omics, reproductive and endocrine health applications, clinical decision support systems, and AI\/ML methodology development. Ensemble methods such as Random Forest and Gradient Boosting showed strong and consistent diagnostic performance. Convolutional neural networks performed well in single-centre settings but showed reduced performance upon external validation, consistent with optimism bias. Overall, 83.6% of studies were classified as high risk of bias and 75.5% lacked external validation. After adjusting for population size, high-income countries produced 23 times more studies per million women of reproductive age than LMICs. Sub-Saharan Africa contributed fewer than 0.1 studies per million women, and no studies were identified from Nepal or Sri Lanka. Current evidence, which is heavily concentrated in high-income and East Asian settings, is insufficient to support population-level deployment. We introduce a four-tier LMIC feasibility classification system that links data modality, infrastructure requirements, and personnel needs. We also propose a phased research agenda that separates near-term priorities (0\u20132 years) from medium-term goals that depend on infrastructure investment.<\/jats:p>","DOI":"10.1371\/journal.pone.0358557","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T20:02:26Z","timestamp":1790366546000},"page":"e0358557","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence for women\u2019s reproductive health: A scoping review of global diagnostic trends, methodological gaps, and a translational research agenda for low-resource settings"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0860-9337","authenticated-orcid":true,"given":"Mohammad Mehedi Hasan","family":"Munna","sequence":"first","affiliation":[{"name":"East West University","place":["Dhaka, Bangladesh"],"department":["Department of Computer Science and Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toriqul","family":"Islam","sequence":"additional","affiliation":[{"name":"East West University","place":["Dhaka, Bangladesh"],"department":["Department of Computer Science and Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9197-183X","authenticated-orcid":true,"given":"Omar","family":"Faruk","sequence":"additional","affiliation":[{"name":"National University Bangladesh","place":["Dhaka, Bangladesh"],"department":["Institute of Science and Technology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reefka Fabliha","family":"Tulona","sequence":"additional","affiliation":[{"name":"Independent University, Bangladesh (IUB)","place":["Dhaka, Bangladesh"],"department":["Department of Computer Science and Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Afsin","family":"Sultana","sequence":"additional","affiliation":[{"name":"National University Bangladesh","place":["Dhaka, Bangladesh"],"department":["Institute of Science and Technology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fairuj","family":"Saima","sequence":"additional","affiliation":[{"name":"National University Bangladesh","place":["Dhaka, Bangladesh"],"department":["Institute of Science and Technology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Acramul Haque","family":"Kabir","sequence":"additional","affiliation":[{"name":"North South University","place":["Dhaka, Bangladesh"],"department":["Department of Electrical and Computer Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tania","family":"Sultana","sequence":"additional","affiliation":[{"name":"East West University","place":["Dhaka, Bangladesh"],"department":["Department 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