AI involves using powerful computers to perform tasks that are typically associated with human intelligence. A type of AI known as deep learning uses complex mathematical algorithms, sometimes called convolutional neural networks, to extract features from data that it is then “trained” on.

This training allows the algorithm to recognize patterns and perform tasks such as analyzing images. In medicine, for example, such algorithms are being studied to see if they can help assess mammograms, detect precancerous tissue in the cervix, or detect cancerous moles more accurately.

To date, AI has been utilized in many medical imaging fields such as CT and magnetic resonance imagery (MRI), and has facilitated accurate diagnosis and treatment.

Advances in both imaging and computers have led to the rise in the potential use of artificial intelligence (AI) in various tasks for example in breast imaging, going beyond the current use in computer‐aided detection to include diagnosis, prognosis, response to therapy, and risk assessment. The automated capabilities of AI offer the potential to enhance the diagnostic expertise of clinicians, including accurate demarcation of tumor volume, extraction of characteristic cancer phenotypes, translation of tumoral phenotype features to clinical genotype implications, and risk prediction. The combination of image‐specific findings with the underlying genomic, pathologic, and clinical features is becoming of increasing value in various cancers. The concurrent emergence of newer imaging techniques has provided radiologists with greater diagnostic tools and image datasets to analyze and interpret. Integrating an AI‐based workflow within different kinds of medical imaging enables the integration of multiple data streams into powerful multidisciplinary applications that may lead the path to personalized patient‐specific medicine.