Novel Insights for Radiation Risk Assessment Unveiled by Deep Learning

Abstract

Contemporary radiation risk assessment predominantly depends on nonlinear parametric models, which typically include a baseline term, a dose-response term, and an effect modifier term. Despite their widespread application in estimating tumor risks, parametric models face a notable drawback: their rigid model structure can be overly restrictive, potentially introducing bias and inaccuracies into risk estimations.