Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges

Cancer continues to be one of the primary causes of death worldwide. Although there has been substantial progress in clinical cancer care, the outcomes for cancer patients still remain poor. The rapid advancements of artificial intelligence (AI) will revolutionize cancer management by addressing current obstacles in oncology research and practice, ultimately enhancing healthcare accuracy and patient outcomes. Increasing evidence demonstrates that AI-based models can improve the accuracy and efficiency of cancer diagnosis and treatment by leveraging multilayer data. Cancer patients could greatly benefit from AI's promising prospects, yet few AI models have been authorized for clinical use. A comprehensive understanding of AI's basic principles, applications, and potential impacts is essential to foster its clinical translation. In this review, we provide an overview of fundamental AI techniques, encompassing machine learning and deep learning. Moreover, we summarize recent studies on AI's transformative role in cancer diagnosis, classification, and personalized treatment planning. Furthermore, we discuss the current challenges that hinder the widespread use of AI, propose potential solutions, and outline future directions. Overall, through systematic analysis of existing preclinical and clinical evidence, this review highlights the substantial potential of AI technology and provides valuable guidance for future research in AI-driven oncology.