Published in PLOS One on 2026-09-25, this study is presented as an attributed evidence briefing. The excerpts below are the original researchers’ words, reproduced under their Creative Commons Attribution licence. Health Signal has selected the abstract and, where needed, opening background paragraphs; the complete methods, results, limitations and references remain in the linked paper.
Original authors: Mohammad Mehedi Hasan Munna, Toriqul Islam, Omar Faruk, Reefka Fabliha Tulona, Afsin Sultana, Fairuj Saima, Acramul Haque Kabir, Tania Sultana, Raihan Ul Islam, Kwang-Sig Lee
Source: PLOS One • DOI 10.1371/journal.pone.0358557
Read the complete primary study ↗ · Creative Commons Attribution licence
From the researchers
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.
1. Introduction
Women’s reproductive and hormonal disorders constitute a significant yet persistently underaddressed global health challenge, with profound implications for fertility, metabolic function, mental health, and quality of life [1–3]. Reproductive and hormonal disorders are complex conditions resulting from a mix of genetic, environmental and lifestyle factors [1,4,5]. Although these are very common, they are highly under-diagnosed and under-treated, resulting in considerable health burden, sub-fertility, disability-adjusted life years (DALYs), and negative health outcomes in later life, such as cardiovascular and metabolic diseases and psychological disturbances [2,5]. PCOS affects 5–18% of women of reproductive age, and is closely associated with a number of other important comorbidities, such as insulin resistance, obesity, dyslipidaemia and depression [1,4,6]. Endometriosis affects 5–10% of women, with diagnostic delays often exceeding 8–12 years [7,8]. Women’s health is a systemic domain where women’s symptoms are often normalised or dismissed [7–9], and hence inadequate care is a common issue affecting these disorders.
Reading this evidence
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This briefing makes the source accessible within the wider technology & industry desk. It does not assess an individual patient or certify a product. When considering relevance to another health system, examine the original authors’ account of context and limitations, then seek local implementation evidence. The related reporting below provides other studies to read alongside this one; it is a topic connection, rather than a claim that their results are directly comparable.
© 2026 Mohammad Mehedi Hasan Munna, Toriqul Islam, Omar Faruk, Reefka Fabliha Tulona, Afsin Sultana, Fairuj Saima, Acramul Haque Kabir, Tania Sultana, Raihan Ul Islam, Kwang-Sig Lee. Excerpts reproduced under CC BY. Selection, formatting and contextual notes by Health Signal. The researchers are the authors of the source study, not members of Health Signal’s staff.
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