Artificial intelligence for precision oncology: beyond patient stratification

Francisco Azuaje, Francisco Azuaje

Abstract

The data-driven identification of disease states and treatment options is a crucial challenge for precision oncology. Artificial intelligence (AI) offers unique opportunities for enhancing such predictive capabilities in the lab and the clinic. AI, including its best-known branch of research, machine learning, has significant potential to enable precision oncology well beyond relatively well-known pattern recognition applications, such as the supervised classification of single-source omics or imaging datasets. This perspective highlights key advances and challenges in that direction. Furthermore, it argues that AI's scope and depth of research need to be expanded to achieve ground-breaking progress in precision oncology.

Conflict of interest statement

The author declares no competing interests.

Figures

Fig. 1
Fig. 1
AI in precision oncology: beyond patient stratification. Selection of key advances and challenges, as well as long-term outlook, discussed in this perspective. Associations between future outlook and challenges are indicated with arrows connecting the former to the latter

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