Marcelo Straus Takahashi, Lane F Donnelly, Selima Siala
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引用次数: 0
Abstract
Artificial intelligence (AI) is increasingly recognized for its transformative potential in radiology; yet, its application in pediatric radiology is relatively limited when compared to the whole of radiology. This manuscript introduces pediatric radiologists to essential AI concepts, including topics such as use case, data science, machine learning, deep learning, natural language processing, and generative AI as well as basics of AI training and validating. We outline the unique challenges of applying AI in pediatric imaging, such as data scarcity and distinct clinical characteristics, and discuss the current uses of AI in pediatric radiology, including both image interpretive and non-interpretive tasks. With this overview, we aim to equip pediatric radiologists with the foundational knowledge needed to engage with AI tools and inspire further exploration and innovation in the field.
期刊介绍：
Official Journal of the European Society of Pediatric Radiology, the Society for Pediatric Radiology and the Asian and Oceanic Society for Pediatric Radiology
Pediatric Radiology informs its readers of new findings and progress in all areas of pediatric imaging and in related fields. This is achieved by a blend of original papers, complemented by reviews that set out the present state of knowledge in a particular area of the specialty or summarize specific topics in which discussion has led to clear conclusions. Advances in technology, methodology, apparatus and auxiliary equipment are presented, and modifications of standard techniques are described.
Manuscripts submitted for publication must contain a statement to the effect that all human studies have been reviewed by the appropriate ethics committee and have therefore been performed in accordance with the ethical standards laid down in an appropriate version of the 1964 Declaration of Helsinki. It should also be stated clearly in the text that all persons gave their informed consent prior to their inclusion in the study. Details that might disclose the identity of the subjects under study should be omitted.
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