Tabriz University of Medical Sciences Journal of Cardiovascular and Thoracic Research 2008-5117 18 3 2026 09 28 Integration of AI with Fetal Echocardiography and MRI for Prenatal CHD Detection: An Updated Review of the Literature 144 152 10.34172/jcvtr.33896 EN Shamsi Ghaffari https://orcid.org/0000-0002-1041-7139 REVIEW 10.34172/jcvtr.33896 2026 02 19 2026 07 04 Detection and diagnosis of congenital heart disease during fetal echocardiography and magnetic resonance imaging are other areas that artificial intelligence has transformed the prenatal healthcare system. This review discusses the application of AI in these imaging approaches with regard to the progress achieved in automated image enhancement and segmentation, as well as the detection of abnormal features. Advanced AI models, especially the deep learning algorithms, have demonstrated encouraging outcomes in terms of diagnostic accuracy, human error reduction, and quicker decisions. In spite of these developments, some challenges have been left, such as data quality, complexity of the algorithm, and the fact that large computational resources are required. There are challenges regarding clinical acceptance, regulatory issues, and the integration of AI into existing workflows. Nevertheless, the opportunities of AI, in particular, custom pregnancy care and the availability of more complex diagnostics, are promising. Further advances in the field of multi-modal AI integration, transfer learning, and ongoing enhancement of algorithms should contribute to better AI application in prenatal CHD detection. The advancement of AI technologies has the potential to transform prenatal diagnostics, leading to improved outcomes for both mothers and newborns. The present review emphasizes the need to conduct further research, take ethical factors into account, and establish effective and consistent regulatory frameworks to guarantee successful and fair usage of AI in clinical practice.