Timing Liu

Abstract Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications2.

Aneuploidy is a major risk factor for pregnancy loss in women with recurrent miscarriages (1). While in vitro fertilization can be offered to achieve reproductive success, a significant proportion of in vitro fertilization embryos are known to be aneuploid (2, 3). This lowers the chances of successful pregnancies and increases the likelihood of having offspring with congenital abnormalities.