In 100 and plus years of stem cell research, scientists are trying to define all the types of stem cells, their markers, differentiation abilities, and possibilities that those cells hold [1]. Induced pluripotent stem cells (iPSCS) are "human-made", induced stem cells that can be produced from different donors and cell types. They have shown to own multiple advantages compared to "natural" stem cells, such as embryonic stem cells (ESCs), adult stem cells, and fetal stem cells. iPSCs were induced by reprogramming of somatic cells, mouse embryonic fibroblasts (MEFs) in 2006, by Takahasi and Yamanaka [2]. The new era started after that, and it is still ongoing. Defining usage of different cells, the best donors, then states of cells during iPSCs reprogramming, and the markers that define them may pave the way for easier reprogramming, thus the focus of research may be on the possibilities that iPSCs hold in regenerative medicine [3], preservation of vulnerable and endangered species [4], drug discovery and testing, disease modeling [3] and cell therapy. One of the ways that can make the path easier, is tools that would collect data, analyze data and give the approximation of the solution. Artificial intelligence which applies machine learning, deep learning and other techniques, may be a valuable factor in producing models and comparing in effectiveness during the reprogramming. Human factor provides data, while artificial intelligence-based methods can bring improvement in accuracy, costs and speed of data analysis [5].