Applying artificial intelligence for cancer immunotherapy |
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Authors: | Zhijie Xu Xiang Wang Shuangshuang Zeng Xinxin Ren Yuanliang Yan Zhicheng Gong |
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Affiliation: | 1. Department of Pathology, Xiangya Hospital, Central South University, Changsha 410008, China;2. Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, China;3. Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China;4. Center for Molecular Medicine, Xiangya Hospital, Key Laboratory of Molecular Radiation Oncology of Hunan Province, Central South University, Changsha 410008, China |
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Abstract: | Artificial intelligence (AI) is a general term that refers to the use of a machine to imitate intelligent behavior for performing complex tasks with minimal human intervention, such as machine learning; this technology is revolutionizing and reshaping medicine. AI has considerable potential to perfect health-care systems in areas such as diagnostics, risk analysis, health information administration, lifestyle supervision, and virtual health assistance. In terms of immunotherapy, AI has been applied to the prediction of immunotherapy responses based on immune signatures, medical imaging and histological analysis. These features could also be highly useful in the management of cancer immunotherapy given their ever-increasing performance in improving diagnostic accuracy, optimizing treatment planning, predicting outcomes of care and reducing human resource costs. In this review, we present the details of AI and the current progression and state of the art in employing AI for cancer immunotherapy. Furthermore, we discuss the challenges, opportunities and corresponding strategies in applying the technology for widespread clinical deployment. Finally, we summarize the impact of AI on cancer immunotherapy and provide our perspectives about underlying applications of AI in the future. |
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Keywords: | Artificial intelligence Cancer immunotherapy Machine learning Diagnostics AI" },{" #name" :" keyword" ," $" :{" id" :" kwrd0035" }," $$" :[{" #name" :" text" ," _" :" artificial intelligence CT" },{" #name" :" keyword" ," $" :{" id" :" kwrd0045" }," $$" :[{" #name" :" text" ," _" :" computed tomography CTLA-4" },{" #name" :" keyword" ," $" :{" id" :" kwrd0055" }," $$" :[{" #name" :" text" ," _" :" cytotoxic T lymphocyte-associated antigen 4 DL" },{" #name" :" keyword" ," $" :{" id" :" kwrd0065" }," $$" :[{" #name" :" text" ," _" :" deep learning ICB" },{" #name" :" keyword" ," $" :{" id" :" kwrd0075" }," $$" :[{" #name" :" text" ," _" :" immune checkpoint blockade irAEs" },{" #name" :" keyword" ," $" :{" id" :" kwrd0085" }," $$" :[{" #name" :" text" ," _" :" immune-related adverse events MHC-I" },{" #name" :" keyword" ," $" :{" id" :" kwrd0095" }," $$" :[{" #name" :" text" ," _" :" major histocompatibility complex class I ML" },{" #name" :" keyword" ," $" :{" id" :" kwrd0105" }," $$" :[{" #name" :" text" ," _" :" machine learning MMR" },{" #name" :" keyword" ," $" :{" id" :" kwrd0115" }," $$" :[{" #name" :" text" ," _" :" mismatch repair MRI" },{" #name" :" keyword" ," $" :{" id" :" kwrd0125" }," $$" :[{" #name" :" text" ," _" :" magnetic resonance imaging PD-1" },{" #name" :" keyword" ," $" :{" id" :" kwrd0135" }," $$" :[{" #name" :" text" ," _" :" programmed cell death protein 1 PD-L1" },{" #name" :" keyword" ," $" :{" id" :" kwrd0145" }," $$" :[{" #name" :" text" ," _" :" PD-1 ligand1 TNBC" },{" #name" :" keyword" ," $" :{" id" :" kwrd0155" }," $$" :[{" #name" :" text" ," _" :" triple-negative breast cancer US" },{" #name" :" keyword" ," $" :{" id" :" kwrd0165" }," $$" :[{" #name" :" text" ," _" :" ultrasonography |
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