Artificial Intelligence, Deep Learning and Precision Medicine

Cancer is often described as a complex and diverse collection of cells, encompassing many distinct subtypes. To address the challenges presented by this heterogeneity, artificial intelligence (AI) has emerged as a pivotal technology for advanced cancer research and clinical management. AI leverages computer systems to perform tasks that traditionally rely on human cognitive abilities. One integral component of AI is Deep Learning (DL), which empowers algorithms to autonomously acquire knowledge from vast datasets, enabling them to make accurate predictions. The application of AI in cancer research has witnessed continuous growth, particularly in the realm of disease prognosis. This advancement has empowered pathologists to precisely diagnose various cancer types, and classify them into different grades and subtypes while considering factors such as invasion patterns, genetic mutations, and metastasis. Such precise characterization of cancers facilitates the implementation of tailored treatment strategies, ultimately leading to more favorable clinical outcomes. Moreover, AI plays a pivotal role in the field of precision medicine, aiding in overcoming challenges like drug resistance and cancer relapse.In comparison to traditional methods, AI offers superior predictive accuracy and enhances the overall clinical perspective. This chapter aims to showcase the evolving roles of AI in diagnosing and prognosticating various cancer types and their subtypes. The applications of AI in cancer prediction warrant further assessment and validation, supporting not only routine tasks for pathologists but also complex diagnostic scenarios. Within these pages, we will highlight various instances where AI, particularly DL, has effectively addressed challenges that were previously deemed insurmountable. Additionally, we will focus on the resources and datasets available to foster a deeper understanding of the intricacies of AI in cancer research. The continued expansion of advanced computational methodologies and AI is expected to facilitate the study of interactomes, significantly enriching our insights into oncology and advancing the concept of personalized medicine.