Medicine has a multidisciplinary approach. In the management of serious diseases, such as cancer, we constantly need to face two crucial aspects in the decision-making process.
On one hand, we should consider what is important in order to treat the disease and, on the other hand, what is important in order to treat the patient. If emotional intelligence refers to the most valuable component required to treat a patient,
nowadays the amount of information relevant for the treatment of a disease exceeds our capacity for an immediate and integrated analysis.
The integration of big data into clinical cancer research offers an unprecedented opportunity to combine information with the complex results derived from research, which require powerful computational resources.
Thanks to its extraordinary analytical power, Artificial Intelligence aims to transform the way we study, diagnose, and treat cancer.
The use of computational learning approaches in preclinical and translational cancer research has rapidly increased in the past few years, leading to an exciting progress in the fields of digital pathology and diagnostics by improving basic research and new drugs development.
The discovery of the complexity of multi-omics and cellular phenotypes or the integration with clinical and behavioural data are deeply changing the cancer research arena.
Not only has Artificial Intelligence generated great expectations to improve cancer diagnosis, prognosis, and treatment, but it has also highlighted some of its most outstanding inherent challenges, such as the potential biases embedded in analytics
datasets, data heterogeneity, and often the limit of external validation cohorts.
Our scientific workshop aims to explore and discuss the different aspects of Artificial Intelligence applied to cancer medicine