Abstract
Drug resistance is one of the key factors affecting the effectiveness of cancer treatment methods, including chemotherapy, radiotherapy, and immunotherapy. Its occurrence is related to factors such as mRNA expression and methylation within cancer cells. If drug resistance in patients can be accurately identified early, doctors can devise more effective treatment plans, which is of great significance for improving patients' survival rates and quality of life. Cancer drug resistance prediction based on artificial intelligence (AI) technology has emerged as a current research hotspot, demonstrating promising application prospects in guiding clinical individualized and precise medication for cancer patients. This review aims to comprehensively summarize the research progress in utilizing AI algorithms to analyze multi-omics data including genomics, transcriptomics, epigenomics, proteomics, metabolomics, radiomics, and histopathology, for predicting cancer drug resistance. It provides a detailed exposition of the processes involved in data processing and model construction, examines the current challenges faced in this field and future development directions, with the aim of better advancing the progress of precision medicine.