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Applying artificial intelligence for cancer immunotherapy
Authors:Zhijie Xu  Xiang Wang  Shuangshuang Zeng  Xinxin Ren  Yuanliang Yan  Zhicheng Gong
Institution: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
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.
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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