18 Technological View

Abstract

Technological advancements in artificial intelligence (AI) have rapidly contributed to the growing body of radiology applications. Deep learning and transfer learning continue to be important components of current AI models that allow algorithms to be more effective and efficient. Compared to traditional machine learning (ML) methods, deep learning involves a multilayer neural network architecture with transfer learning often used by pretraining on extensive datasets. Radiomics and Clinical Decision Support Systems have emerged from the success of AI algorithms as clinically significant applications. However, logistic difficulties related to data acquisition and annotation and interpretability of AI algorithms are major difficulties that must be addressed.

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