Artificial intelligence-assisted fetal ultrasound is emerging as a scalable diagnostic solution for low-resource settings, where obstetric ultrasound services are often concentrated in urban referral centers and rural facilities lack functioning equipment, reliable electricity, or trained personnel. The World Health Organization recommends at least one ultrasound scan before 24 weeks of gestation for all pregnant women, yet access remains unequal, with the burden of neonatal and perinatal disorders concentrated in sub-Saharan Africa and South Asia. Early deep-learning work was motivated by this access gap: Yaqub et al. developed an automatic verification system for fetal neurosonographic planes, while van den Heuvel et al. demonstrated that fully convolutional neural networks could detect the fetal head and estimate head circumference from free-hand sweep data collected by minimally trained health workers in Wolisso, Ethiopia, using an acquisition protocol teachable within a single day. This established the blind-sweep AI paradigm later scaled by multicenter studies. Subsequent work extended automated quality control to fetal cardiac four-chamber views and first-trimester three-dimensional volumetric assessment, while weakly supervised and self-supervised learning addressed the scarcity of expert-annotated data. Unlike traditional ultrasound training requiring years of supervised practice, AI-enabled devices can guide novice operators through standardized acquisition protocols and generate clinically actionable outputs.