Interpretable artificial intelligence in radiology and radiation oncology
Sunan Cui, Alberto Traverso, Dipesh Niraula, Jiaren Zou, Yi Luo, Dawn Owen, Issam El Naqa, Lise Wei
Abstract
Artificial intelligence has been introduced to clinical practice, especially radiology and radiation oncology, from image segmentation, diagnosis, treatment planning and prognosis. It is not only crucial to have an accurate artificial intelligence model, but also to understand the internal logic and gain the trust of the experts. This review is intended to provide some insights into core concepts of the interpretability, the state-of-the-art methods for understanding the machine learning models, the evaluation of these methods, identifying some challenges and limits of them, and gives some examples of medical applications.
Topics & Concepts
InterpretabilityRadiation oncologyArtificial intelligenceMedical physicsMedicineRadiologyComputer scienceMedical imagingRadiation therapyRadiomics and Machine Learning in Medical ImagingExplainable Artificial Intelligence (XAI)Medical Imaging and Analysis