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Review
. 2022 Feb 10;8(4):FSO787.
doi: 10.2144/fsoa-2021-0074. eCollection 2022 Apr.

An overview of artificial intelligence in oncology

Eduardo Farina  1 , Jacqueline J Nabhen  2 , Maria Inez Dacoregio  3 , Felipe Batalini  4 , Fabio Y Moraes  5
Affiliations
Review

An overview of artificial intelligence in oncology

Eduardo Farina et al. Future Sci OA. .

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.

Keywords: artificial intelligence; cancer diagnosis; data integration; medical oncology; patient stratification; precision oncology.

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Conflict of interest statement

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.

Figures

Figure 1.
Figure 1.. Artificial intelligence flywheel.
Graphic representation of the artificial Intelligence and data cycle for building effective and responsible machine learning models for healthcare.
Figure 2.
Figure 2.. Potential applications of artificial intelligence in a cancer patient's journey.
AI-based models can be used in preclinical (orange box) and in clinical scenarios, both before and after cancer diagnosis (green and blue boxes, respectively). In real-life oncology care, AI has the potential to optimize risk stratification, screening recommendations, diagnosis, prognosis, decision-making and treatment-related outcome prediction. Connecting clinical research to routine oncology practice by efficient drug repurposing, accelerated new treatment discovery and efficient patient matching to RCTs is another potential contribution of AI. AI: Artificial intelligence; RCT: Randomized controlled trial.
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