1. Applying artificial intelligence for cancer immunotherapy
- Author
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Xiang Wang, Zhicheng Gong, Yuanliang Yan, Xinxin Ren, Shuangshuang Zeng, and Zhijie Xu
- Subjects
Risk analysis ,Artificial intelligence ,CTLA-4, cytotoxic T lymphocyte-associated antigen 4 ,Computer science ,medicine.medical_treatment ,Diagnostic accuracy ,Cancer immunotherapy ,Review ,MMR, mismatch repair ,RM1-950 ,MHC-I, major histocompatibility complex class I ,TNBC, triple-negative breast cancer ,PD-1, programmed cell death protein 1 ,03 medical and health sciences ,0302 clinical medicine ,Machine learning ,medicine ,ICB, immune checkpoint blockade ,ML, machine learning ,General Pharmacology, Toxicology and Pharmaceutics ,Diagnostics ,030304 developmental biology ,0303 health sciences ,DL, deep learning ,US, ultrasonography ,business.industry ,CT, computed tomography ,irAEs, immune-related adverse events ,ComputingMethodologies_PATTERNRECOGNITION ,Software deployment ,030220 oncology & carcinogenesis ,AI, artificial intelligence ,Health information ,Therapeutics. Pharmacology ,PD-L1, PD-1 ligand1 ,business ,MRI, magnetic resonance imaging - 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., Graphical abstract With the increasing of clinical data and advanced AI methodologies, AI-based technologies have the potential to increase the functional roles in cancer immunotherapy response.Image 1
- Published
- 2021