Artificial Intelligence-Assisted Fetal Ultrasound in Low-Resource Settings: Opportunities, Challenges, and Future...

Artificial intelligence (AI) has emerged as a promising strategy to improve access to fetal ultrasound in low-resource settings, where shortages of trained personnel, limited infrastructure, and unequal access to diagnostic imaging continue to compromise maternal and fetal healthcare. This narrative review synthesizes current evidence on AI-assisted fetal ultrasound, focusing on its clinical applications, implementation experience, challenges, and future directions. The reviewed evidence demonstrates that AI can support multiple stages of the fetal ultrasound pathway, including gestational age estimation, automated biometry and image quality assessment, structural anomaly detection, fetal cardiac and movement monitoring, image enhancement, and edge deployment for resource-constrained environments. These technologies have shown encouraging diagnostic performance and the potential to facilitate task-shifting by enabling non-specialist healthcare providers to acquire and interpret ultrasound examinations with greater accuracy and consistency. However, widespread implementation remains constrained by limited representation of low-resource populations in training datasets, insufficient prospective multicenter validation, infrastructure and connectivity limitations, workforce training requirements, and unresolved regulatory, ethical, and governance issues. Future research should emphasize the development of locally representative datasets, prospective multicentre validation, resource-efficient AI models, robust regulatory frameworks, and implementation studies that assess real-world feasibility. In addition, health economic evaluations, including cost-effectiveness analyses, are needed to determine the affordability and sustainability of AI-assisted fetal ultrasound in resource-constrained healthcare systems. With these advances, AI-assisted fetal ultrasound could play an important role in expanding equitable access to quality antenatal imaging and improving maternal and fetal healthcare in low-resource settings.