Advances in artificial intelligence to predict cancer immunotherapy efficacy - Xie, Jindong; Luo, Xiyuan; Deng, Xinpei; Tang, Yuhui; Tian, Wenwen; Cheng, Hui; Zhang, Junsheng; Zou, Yutian; Guo, Zhixing; Xie, Xiaoming (Frontiers in Immunology, 2023)
Tumor immunotherapy, particularly the use of immune checkpoint inhibitors, has yielded impressive clinical benefits. Therefore, it is critical to accurately screen individuals for immunotherapy sensitivity and forecast its efficacy. With the application of artificial intelligence (AI) in the medical field in recent years, an increasing number of studies have indicated that the efficacy of immunotherapy can be better anticipated with the help of AI technology to reach precision medicine. This article focuses on the current prediction models based on information from histopathological slides, imaging-omics, genomics, and proteomics, and reviews their research progress and applications. Furthermore, we also discuss the existing challenges encountered by AI in the field of immunotherapy, as well as the future directions that need to be improved, to provide a point of reference for the early implementation of AI-assisted diagnosis and treatment systems in the future.