FemTech Radar research feed.https://chanmeng666.github.io/femtech-radar/http://arxiv.org/abs/2606.29580v1http://arxiv.org/abs/2606.29580v1Deploying robust, offline AI tools for midwives in low-resource settings can directly improve maternal and newborn outcomes, bridging gaps in care. Maternal and newborn mortality remain among the highest in sub-Saharan Africa, where midwifery care is often delivered by nurses who lack midwifery training to international standards, and consulting authoritative guidance at the point of care is hard: the guidelines are long…Sun, 28 Jun 2026 19:52:04 GMTresearchhttp://arxiv.org/abs/2606.29467v1http://arxiv.org/abs/2606.29467v1Open benchmarks for maternal health AI enable more robust, transparent, and equitable evaluation of tools that can impact care quality for women and children. Medical question-answering benchmarks rarely cover the maternal, neonatal, child, and reproductive-health questions a nurse-midwife asks, and, to our knowledge, no public chunk-level relevance benchmark exists for maternal-health guideline retrieval. We release two benchmarks…Sun, 28 Jun 2026 15:51:53 GMTresearchhttp://arxiv.org/abs/2606.25254v1http://arxiv.org/abs/2606.25254v1Advances in annotation-efficient ultrasound AI can make fetal monitoring more accessible and accurate, especially in resource-limited settings. Maternal-fetal US is the primary imaging modality for monitoring fetal development, yet accurate automated segmentation remains challenging due to the scarcity of pixel-level annotations. To address this issue, we propose DACL, a semi-supervised framework for robust fetal US…Wed, 24 Jun 2026 00:26:00 GMTresearch