Orofacial clefts are among the most common congenital craniofacial abnormalities, yet accurate prenatal detection remains challenging due to the scarcity of experienced specialists and the relative rarity of the condition. Early and reliable diagnosis is essential to enable timely clinical intervention and reduce associated morbidity. Here we show that an artificial intelligence system, trained on over 45,139 ultrasound images from 9,215 fetuses across 22 hospitals, can diagnose fetal orofacial clefts with sensitivity and specificity exceeding 93% and 95% respectively, matching the performance of senior radiologists and substantially outperforming junior radiologists. When used as a medical copilot, the system raises junior radiologists' sensitivity by more than 6%. Beyond direct diagnostic assistance, the system also accelerates the development of clinical expertise. A pilot study involving 24 radiologists and trainees demonstrated that the model can improve the expertise development for rare conditions. This dual-purpose approach offers a scalable solution for improving both diagnostic accuracy and specialist training in settings where experienced radiologists are scarce.
@article{arxiv.2603.06522,
title = {Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education},
author = {Yuanji Zhang and Yuhao Huang and Haoran Dou and Xiliang Zhu and Chen Ling and Zhong Yang and Lianying Liang and Jiuping Li and Siying Liang and Rui Li and Yan Cao and Yuhan Zhang and Jiewei Lai and Yongsong Zhou and Hongyu Zheng and Xinru Gao and Cheng Yu and Liling Shi and Mengqin Yuan and Honglong Li and Xiaoqiong Huang and Chaoyu Chen and Jialin Zhang and Wenxiong Pan and Alejandro F. Frangi and Guangzhi He and Xin Yang and Yi Xiong and Linliang Yin and Xuedong Deng and Dong Ni},
journal= {arXiv preprint arXiv:2603.06522},
year = {2026}
}