Abstract
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.