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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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Artificial intelligence: a primer for pediatric radiologists.

Marcelo Straus Takahashi1, Lane F Donnelly2, Selima Siala2

  • 1University of North Carolina, 200 Old Clinic, CB #7510, Chapel Hill, NC, 27599, USA. mst@ad.unc.edu.

Pediatric Radiology
|November 18, 2024
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Summary
This summary is machine-generated.

This manuscript introduces pediatric radiologists to artificial intelligence (AI) concepts and challenges. It aims to provide foundational knowledge for engaging with AI tools in pediatric imaging.

Keywords:
Artificial intelligenceChildData scienceDeep learningMachine learningNatural language processingRadiology

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Pediatric Imaging

Background:

  • Artificial intelligence (AI) has significant potential in radiology, but its use in pediatric radiology is limited.
  • Pediatric radiology faces unique challenges in AI application, including data scarcity and distinct clinical characteristics.

Purpose of the Study:

  • To introduce pediatric radiologists to essential AI concepts.
  • To outline challenges and current uses of AI in pediatric imaging.
  • To equip radiologists with foundational knowledge for AI engagement.

Main Methods:

  • Review of AI concepts: use case, data science, machine learning, deep learning, natural language processing, generative AI.
  • Discussion of AI training and validation basics.
  • Outline of unique challenges in pediatric AI application.
  • Overview of current AI uses in pediatric radiology (interpretive and non-interpretive tasks).

Main Results:

  • AI concepts such as machine learning and natural language processing are relevant to pediatric radiology.
  • Data scarcity and unique clinical characteristics present challenges for AI implementation.
  • AI is currently used for both interpretive and non-interpretive tasks in pediatric radiology.

Conclusions:

  • Pediatric radiologists need foundational AI knowledge to utilize AI tools effectively.
  • Further exploration and innovation in AI for pediatric imaging are encouraged.
  • Understanding AI basics is crucial for the advancement of pediatric radiology.