Straus Takahashi Marcelo, Donnelly Lane F, Siala Selima
University of North Carolina, 200 Old Clinic, CB #7510, Chapel Hill, NC, 27599, USA.
Pediatr Radiol. 2024 Dec;54(13):2127-2142. doi: 10.1007/s00247-024-06098-x. Epub 2024 Nov 18.
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.
人工智能(AI)在放射学中的变革潜力日益得到认可;然而,与整个放射学领域相比,其在儿科放射学中的应用相对有限。本文向儿科放射科医生介绍人工智能的基本概念,包括用例、数据科学、机器学习、深度学习、自然语言处理和生成式人工智能等主题,以及人工智能训练和验证的基础知识。我们概述了在儿科成像中应用人工智能的独特挑战,如数据稀缺和独特的临床特征,并讨论了人工智能在儿科放射学中的当前应用,包括图像解读和非解读任务。通过这一概述,我们旨在为儿科放射科医生提供使用人工智能工具所需的基础知识,并激发该领域的进一步探索和创新。