An Overview of Artificial Intelligence in Oncology

Abstract

Cancer is associated with significant morbimortality globally. Advances in screening, diagnosis, management and survivorship were substantial in the last decades, however, challenges in providing personalized and data-oriented care remain. Artificial intelligence (AI), a branch of computer science used for predictions and automation, has emerged as potential solution to improve the healthcare journey and to promote precision in healthcare. AI applications in oncology include, but are not limited to, optimization of cancer research, improvement of clinical practice (eg., prediction of the association of multiple parameters and outcomes – prognosis and response) and better understanding of tumor molecular biology. In this review, we examine the current state of AI in oncology, including fundamentals, current applications, limitations and future perspectives.

Plain Language Summary

Cancer is associated with significant morbimortality globally. Although significant advances occurred in the last decades, challenges in providing personalized care remain. Artificial intelligence (AI) has emerged as a mean of improving cancer care using compure science. AI applications in oncology include, but are not limited to, optimization of cancer research, improvement of clinical practice (including prediction of cancer patients outcomes and response to treatment) and better understanding of tumor characteristics. In this review, we explored the current state of AI in oncology, including fundamentals, current applications, limitations and future perspectives.

Keywords:

• artificial intelligence
• cancer diagnosis
• data integration
• medical oncology
• patient stratification
• precision oncology

Author contributions

E Farina, JJ Nabhen, MI Dacoregio: conceptualization, literature search, data curation and writing of the original draft. F Batilini: supervision and review and editing. FY Moraes: conceptualization, project administration, supervision and review and editing. All authors read and approved the final manuscript.

Financial & competing interests disclosure

Felipe Batalini: reporting – Curio Science (consulting); Fabio Ynoe de Moraes: Elekta Ltd (consulting) and Astra Zeneca (honorarium). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.