Artificial Intelligence Methods for Process Automation: A Systematic Literature Review (#420)

Authors

Villegas-Chavez, Ricardo Alexander

Dios-Castillo, Cristian Abraham

Abstract

The automation of processes with artificial intelligence (AI) allows companies' operations to become more accurate and efficient, in this systematic literature review, an exhaustive analysis was made using the PRISMA method, the characteristics of automating a process were studied, It was found that automating a process in its abstract nature is inclined towards accuracy, robustness of the process as the model improves when processing new data and finally to efficiency, likewise an analysis of AI methods that are present in the literature in the last year (2024-205) was made, it was revealed that the most used methods are Machine learning, It was revealed that the most used methods are Machine learning, which receives and returns structured data, and deep learning being the most used and processing mostly unstructured input data and returning almost all structured data, it was also found that the family of deep learning models most present in the literature is YOLO and for machine learning the decision trees, it was found that the 4 most effective deep learning models, all with an effectiveness value of 100% are “Darknet-19”, “Resnet-18”, “Resnet-50” and “Resnet-10”, these were applied to the Health area and for Machine learning “Extra Trees” with 100% oriented to the health area being the area with more presence in the experiments with AI models to automate processes in the scientific literature.