Artificial intelligence in oncology

• Published in: Cancer science Vol. 111; no. 5; pp. 1452 - 1460
• Main Authors: Shimizu, Hideyuki, Nakayama, Keiichi I.
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.05.2020; John Wiley and Sons Inc
• Subjects: Algorithms; Artificial intelligence; Cancer; Deep learning; Dermatology; Industrialized nations; Machine learning; Mammography; Medical research; Medical screening; Neural networks; Oncology; Pathology; personalized medicine; Researchers; Review; deep learning; machine learning; artificial intelligence; oncology; personalized medicine
• ISSN: 1347-9032; 1349-7006; 1349-7006
• DOI: 10.1111/cas.14377

Artificial intelligence (AI) has contributed substantially to the resolution of a variety of biomedical problems, including cancer, over the past decade. Deep learning, a subfield of AI that is highly flexible and supports automatic feature extraction, is increasingly being applied in various areas of both basic and clinical cancer research. In this review, we describe numerous recent examples of the application of AI in oncology, including cases in which deep learning has efficiently solved problems that were previously thought to be unsolvable, and we address obstacles that must be overcome before such application can become more widespread. We also highlight resources and datasets that can help harness the power of AI for cancer research. The development of innovative approaches to and applications of AI will yield important insights in oncology in the coming decade. Artificial intelligence (AI) has contributed substantially to the resolution of a variety of biomedical problems, including cancer, over the past decade. In this review, we describe numerous recent examples of the application of AI in oncology, including cases in which deep learning has efficiently solved problems that were previously thought to be unsolvable, and we address obstacles that must be overcome before such application can become more widespread.