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Abstract

The fourth most common cause of cancer related deaths globally is liver cancer. Recent developments in Artificial Intelligence (AI) have sparked the creation of algorithms for the treatment of cancer. Through diagnostic image analysis, biomarker identification and the prediction of individual clinical outcomes, a growing corpus of recent studies has assessed Machine Learning (ML) and Deep Learning (DL) algorithms for pre-screening, diagnosis, and management of liver cancer patients. Despite the potential of these early AI technologies, more effort still has to be done to deploy AI and explain its "black box" in order to achieve full clinical translatability. In addition, given that they still mostly rely on protracted trial and error trials, certain developing sectors, such as RNA nanomedicine for targeted liver cancer therapy, may benefit from the use of AI. In this work, we present the current state of AI in liver tumors as well as the difficulties associated with its detection and treatment. We've covered the potential applications of AI in liver cancer in the future and how a multidisciplinary strategy utilizing AI in nanomedicine might hasten the translation of personalized liver cancer medicine from the bench to the clinic.