Present and future perspectives of artificial intelligence: examples of mathematical approaches for analysis of disease dynamics

Abstract

Artificial intelligence (AI) has been applied widely in medicine. For example, deep neural network-based deep learning is particularly effective for pattern recognition in static medical images. Additionally, dynamic time series data are analysed ubiquitously in biology and medicine, as in the application of BCR-ABL International Scale time series data measured from CML patients treated with tyrosine-kinase inhibitors. Nonlinear data analyses, rather than conventional deep learning, can be more powerful for this type of dynamic disease information. Here, I introduce our mathematical approaches that are applicable for disease dynamics, such as dynamical network biomarkers (DNB) and randomly distributed embedding (RDE), as examples of nonlinear data analyses. I also discuss the availability of neuroinspired and neuromorphic hardware systems, which we are developing for potential use in next-generation AI.