Use of AI and ML in Oncology: Detection, Treatment and Study

Cancer continues to be one of the leading causes of death worldwide, largely because many cases are diagnosed at advanced stages and because tumors are biologically complex. In recent years, artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has significantly improved the way cancer is detected, analyzed, and managed [ref no. 1]. AI-based systems can interpret medical images such as CT scans, MRI scans, mammograms, and X-rays with high precision, enabling earlier detection of abnormalities [ref no. 5]. Beyond imaging, AI also assists in analyzing genomic and molecular data to identify mutations, predict disease progression, and guide targeted therapies [ref no. 2]. These advancements contribute directly to precision oncology, where treatment strategies are tailored to individual patients. Although challenges such as data bias, limited interpretability, and ethical concerns persist [ref no. 6], AI-driven oncology represents a major step toward more accurate, efficient, and personalized cancer care.