Intelligent Radiation: A review of Machine learning applications in nuclear and radiological sciences
Abbas J. Jinia, Shaun D. Clarke, Jean M. Moran, Sara A. Pozzi
Abstract
Modern advancements in computing power and the ability of machine learning (ML) to model complex relationships between input and output have opened new prospects for data processing. This ML technology finds applications in nuclear and radiological sciences to extract meaningful information from data and drive intelligent decision-making. The literature review performed in the present manuscript encompasses key areas, including nuclear power, nuclear security, international safeguards, and the use of radiological sciences in healthcare. The applications discussed range from predictive modeling of nuclear processes to enhancing image reconstruction and analysis in medical imaging. The article also focuses on highlighting key studies and methodologies, offering a demonstration of various ML models in handling the unique challenges posed by nuclear and radiological data. The goal is to provide a comprehensive review, which can serve as a guide for researchers, providing a deeper understanding of the impact and potential of ML in the field.