The Evolution and Reliability of Machine Learning Techniques for Oncology

is no secret that the rise of the Internet and other digital technol-ogies has sparked renewed interest in AI-based techniques, especially those that fall under the umbrella of the subset of algorithms known as "Machine Learn-ing" (ML). Electronic innovations have enabled us to comprehend the universe beyond the limits of human cognition. The difficult nature of a high-dimensional dataset. Although these techniques have been regularly employed by the medical sciences, their adoption to enhance patient care has been a bit slow. The avail-ability of curated diverse data sets for model development is all examples of the substantial hurdles that have delayed these efforts. The future clinical accep-tance of each of these characteristics may be affected by a number of limiting conditions, such as the time and resources spent on data collection and model development, the cost of integration relative to the time and resources spent on translation, and the potential for patient damage. In order to preserve value and enhance medical care, the goal of this article is to evaluate all facets of the issue in light of the validity of using ML methods in cancer, to serve as a template for further research and the subfield of oncology that serves as a model for other parts of the discipline.