人工智能用于女性生殖健康：全球诊断趋势、方法学缺口及面向低资源环境的转化研究议程综述
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
多囊卵巢综合征、子宫内膜异位症、甲状腺疾病、不孕和妊娠并发症等女性生殖与内分泌疾病是全球重大健康负担，在中低收入国家尤其难以诊治。作者按系统综述规范检索五个数据库，纳入2021至2025年间116项同行评审研究，分为临床预测与风险建模、医学影像、生物标志物与多组学、生殖内分泌应用、临床决策支持和人工智能方法学六类。随机森林和梯度提升等集成方法表现稳定，卷积神经网络在单中心表现好但外部验证时性能下降。83.6%的研究存在高偏倚风险，75.5%缺乏外部验证；按人口规模调整后，高收入国家每百万育龄女性的研究产出是中低收入国家的23倍，撒哈拉以南非洲不足0.1，尼泊尔和斯里兰卡没有研究。作者提出面向低资源环境的四级可行性分类和分阶段研究议程。
为什么推荐给您：系统性揭示女性生殖健康人工智能研究的地域与验证缺口，并提出转化议程，属重要方法学与公平性发现。
不需要生物学背景，多打比方
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摘要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.