Lecture : The clinical environment depends on many emerging technologies including deep learning, machine learning, informatics, clinical decision support system, radiomics, and imaging biomarkers to meet various unmet needs. In actual clinical environment, the usability of archived data is, however, far from satisfaction. In the field of oncology, the informatics or images are referred only once by a radiologist or physician for diagnosis or treatment and then discarded into the repository. We need to change this clinical environment with these technologies. Recent advances in engineering technology including deep learning, machine learning, informatics, clinical decision support system, and imaging biomarkers allow us to converge them beyond conventional disciplinary boundaries. Here, I will talk about new technology classified as artificial intelligence. Artificial intelligence with big-data analysis in HCC management including electric medical record, images, signals, etc will open a new era for decision support for cancer treatment, survival prediction as well as diagnostic, pathologic, surgical imaging in hospital. I’ll review technical challenges for deep learning researches and our research results in Asan medical center, South Korea. In addition, for needs of treatment response prediction and decision supports, informatics based on fully automated segmentation and detection could be provided to physicians without any additional costs. In this talk, I will cover recent updates of artificial intelligence in HCC management .