Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Abstract

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, "precision medicine," a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

Keywords: artificial intelligence; deep learning; machine learning; omics; pathology; precision medicine; radiology.

Figure 1

Workflow diagram of a typical prostate CADx system in Reference 44 (Wang et al., 2014); this is a modified figure from the reference. The orange squares indicate the data (original scan and pre-processed image), and the light blue squares indicate the data and processing applied to the image.

Figure 2

Examples of how machine learning and deep learning techniques are being used to reduce the dimensionality of massive omics data to extract features and then stratify the patients. (A) An example of analysis using Kaplan–Meier curves after dimensionality reduction and feature concentration using machine learning and deep learning techniques applied to genetic mutation information. (B) An example of patient stratification using machine learning techniques to extract features from multilayer omics data.

Figure 3

Application of machine learning and Bayesian statistics in different phases of drug development.

Figure 4

Illustration of the proposed network in Reference 188 (Qin et al., 2020); this is a modified figure from the reference. (A) Illustration of the proposed method: a cross-domain confounding representation is generated by constraining the cross-domain mapping reconstruction. (B) Domain confounding representation through cross-domain mapping and classification cycle consistency. Encoder F and decoder G constitute the variational automatic encoder (VAE) architecture for unsupervised representation learning. The D module constitutes the GAN discriminator and the C module constitutes the classifier.