Indeed, in this particular discipline, ML algorithms have evolved faster. In Schmauch et al, 2020, the authors predicted the RNA-Seq expression of tumours from whole slide images using a deep learning model as well. For instance, in Morilla et al, a minimal signature composed of seven miRNAs and two biological indicators was identified using general linear models trained at the base of a deep learning model to predict treatment outcomes in gastrointestinal cancer. Their application has revealed themselves as an effective way to tackle multiple clinical questions, from diagnosis to prediction of treatment outcomes. These methods are being successfully used in cancer at many levels by simply analysing clinical data, biological indicators, or whole slide images. The former is a collection of data-driven techniques with the goal of building predictive models from high-dimensional datasets, while the latter refers to the grade of human intervention that these models require to make predictions. Medical practitioners are becoming familiar with some few mathematical concepts, such as machine learning (ML) or (un/semi) supervised learning.

By integrating novel structures of data organisation, exploitation, and sharing of clinical data among health institutions, AI is achieving in the short-term to successfully accelerate cancer research. More and more every day, the contribution of AI-based models to different axes of cancer research is not only improving their ability to stratify patients early on or discover new drugs but also influences its fundamentals. The flourishing proliferation of artificial intelligence (AI) worldwide over the last decade has disrupted the way oncologists face cancer.

Ali 3328 editor by