Published online Jun 28, 2020. doi: 10.35713/aic.v1.i1.1
Peer-review started: May 20, 2020
First decision: June 4, 2020
Revised: June 9, 2020
Accepted: June 12, 2020
Article in press: June 12, 2020
Published online: June 28, 2020
Cancer is a major public health problem worldwide. Current predictions suggest that 13 million people will die each year from cancer by 2030. Thus, new ideas are urgently needed to change paradigms in the global fight against cancer. Over the last decades, artificial intelligence (AI) emerged in the field of cancer research as a new and promising discipline. Although emerging, a great potential is appreciated in AI to improve cancer diagnosis and prognosis, as well as to identify relevant therapeutics in the current era of personalized medicine. Developing pipelines connecting patient-generated health data easily translatable into clinical practice to assist clinicians in decision making represents a challenging but fascinating task. AI algorithms are mainly fueled by multi omics data which, in the case of cancer research, have been largely derived from international cancer programs, including The Cancer Genome Atlas (TCGA). Here, I briefly review some examples of supervised and unsupervised big data derived from TCGA programs and comment on how AI algorithms have been applied to improve the management of patients with cancer. In this context, Artificial Intelligence in Cancer journal was specifically launched to promote the development of this discipline, by serving as a forum to publish high-quality basic and clinical research articles in various fields of AI in oncology.
Core tip: Artificial intelligence (AI) emerged in the field of cancer research as a new and promising discipline to improve the management of patients with cancer, including more accurate and fastest diagnosis to facilitate the therapeutic decision. AI models are mainly fueled by multi omics data. Integrating omics data and clinical data of patients represents a challenging but fascinating task.