Craniofacial dysmorphism recognition is the first step in diagnosing most genetic syndromes. However, the number of genetic syndromes is enormous, and the specific facial features are difficult to memorize. For clinical practice, recent advances in artificial intelligence can be of use. One such tool, Face2Gene (FDNA, Inc., Boston, MA), is an innovative free group of applications, that helps ... [Show full abstract] clinicians recognize possible genetic syndromes from patients' facial two-dimensional photos. The initial data set used to train this technology consisted primarily of Caucasian patients. Because ethnic differences affect patients' facial features, the recognition probability in Asian patients may be limited. Our aim was to test the technology's recognition probability on Thai children with Down Syndrome (DS) as compared to Thai children without DS (non-DS). Two separate control groups of Thai non-DS children, either unaffected or having other syndromes, were included. Frontal photographs were obtained from all the participants. All 30 children with DS were recognized as DS in the top 10 syndrome-matches (100% sensitivity), and 27 were in the first ranking of suggested syndromes. Eighteen non-DS were recognized as DS (87.2% specificity) with an accuracy of 89%. We present a scientific basis for this novel tool, useful in the clinic where patients are of a different ethnicity unfamiliar to the evaluator. However, Face2Gene cannot be considered a replacement for clinicians' knowledge of phenotypes. Further studies on other genetic syndromes/ethnicities being identified by software algorithms are needed.