Speaker
Description
Artificial intelligence methods are increasingly used in physics to support classification, prediction, uncertainty handling, and interpretable decision-making. In medical physics, radiation risk assessment often depends on multiple interacting parameters, including absorbed dose, exposure duration, source activity, irradiated skin area, radiation energy, source-to-skin distance, shielding conditions, and contamination geometry. Conventional numerical approaches provide useful dose estimates but may be less effective when expert reasoning, uncertainty, and transparent risk classification are required.
This study proposes an explainable fuzzy-AI decision-support framework for skin radiation risk classification in medical physics applications. The model integrates relevant dosimetric and exposure-related parameters within a hierarchical fuzzy reasoning structure. Input variables are represented using linguistically interpretable membership functions, and rule-based inference is applied to classify skin radiation risk into clinically meaningful categories. The framework is designed to support transparent decision-making by explicitly showing how different combinations of dose, activity, exposure time, shielding, and geometry contribute to the final risk level.
The proposed approach is particularly relevant for scenarios involving occupational exposure, external contamination, radiopharmaceutical handling, and radiation protection training. By combining fuzzy logic with AI-assisted decision support, the framework provides an interpretable alternative to purely black-box classification methods. It may also contribute to educational and practical tools for radiation protection, where explainability, uncertainty awareness, and rapid risk stratification are essential.
The study demonstrates how explainable AI concepts can be adapted to medical physics and radiation safety problems, providing a bridge between computational intelligence and physics-based risk assessment