Artificial intelligence for women’s reproductive health: A scoping review of global diagnostic trends, methodological gaps, and a translational research agenda for low-resource settings
4 affiliations
- 1Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh
- 2Institute of Science and Technology, National University Bangladesh, Dhaka, Bangladesh
- 3Department of Computer Science and Engineering, Independent University, Bangladesh (IUB), Dhaka, Bangladesh
- 4Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh
Abstract
Women’s 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–guided scoping review of artificial intelligence (AI) and machine learning (ML) applications in women’s 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 (0–2 years) from medium-term goals that depend on infrastructure investment.
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BibTeX
@article{munna2026artificial,
title = {{Artificial intelligence for women’s reproductive health: A scoping review of global diagnostic trends, methodological gaps, and a translational research agenda for low-resource settings}},
author = {Mohammad Mehedi Hasan Munna and Toriqul Islam and Omar Faruk and Reefka Fabliha Tulona and Afsin Sultana and Fairuj Saima and Acramul Haque Kabir and Tania Sultana and Raihan Ul Islam},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0358557},
url = {https://doi.org/10.1371/journal.pone.0358557},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial intelligence for women’s reproductive health: A scoping review of global diagnostic trends, methodological gaps, and a translational research agenda for low-resource settings
AU - Mohammad Mehedi Hasan Munna
AU - Toriqul Islam
AU - Omar Faruk
AU - Reefka Fabliha Tulona
AU - Afsin Sultana
AU - Fairuj Saima
AU - Acramul Haque Kabir
AU - Tania Sultana
AU - Raihan Ul Islam
PY - 2026
DA - 2026/09/25
JO - PLOS One
DO - 10.1371/journal.pone.0358557
UR - https://doi.org/10.1371/journal.pone.0358557
AB - Women’s 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–guided scoping review of artificial intelligence (AI) and machine learning (ML) applications in women’s 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 (0–2 years) from medium-term goals that depend on infrastructure investment.
SN - 1932-6203
ER -