AI and ML can effectively detect and diagnose Polycystic Ovary Syndrome (PCOS), which is the most common hormone disorder among women, typically between ages 15 and 45. Researchers suggested integrating large population-based studies with electronic health datasets and analyzing common laboratory tests to identify sensitive diagnostic biomarkers that can facilitate the diagnosis of PCOS. The researchers conducted a systematic review of all peer-reviewed studies published on this topic for the past 25 years that used AI or ML to detect PCOS. With the help of an experienced NIH librarian, the researchers identified potentially eligible studies. In total, they screened 135 studies and included 31 in this paper. All studies were observational and assessed the use of AI and ML technologies in patient diagnosis. Ultrasound images were included in about half the studies. Among the 10 studies that used standardized diagnostic criteria to diagnose PCOS.