Abstract
The field of genomic medicine produces large datasets, which need to be rapidly analysed to produce clinically actionable insights in cancer care. Artificial intelligence thrives on data, processing and learning from datasets with a degree of accuracy and efficiency that traditional computing algorithms can not achieve. Based on a patient's genome sequence, AI could allow earlier detection of cancer, inform personalised treatment plans and provide insights into prognostication. However, this valuable tool is met with skepticism, with stakeholders concerned over data security, liability for AI's mistakes due to hallucination and the threat to clinical jobs. This review highlights both the benefits and potential problems of using AI in genomic medicine for cancer care, with the aim to lessen the knowledge gap between clinicians and data scientists and facilitate the future deployment of AI in cancer care.