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Final ID: Poster #: EDU-044

How to Interpret Research Papers in Artificial Intelligence (What Pediatric Radiologists Need to Know)

Purpose or Case Report: Artificial intelligence (AI) has the potential to improve many aspects of pediatric radiology. AI solutions have emerged to perform a wide range of tasks, including facilitating the workload of radiologists, improving image quality, performing motion correction, and reducing contrast dose. While a myriad of courses for learning AI are available, most resources lack the necessary radiology perspective required for clinical application. Few interactive resources are available to teach a systematic approach for pediatric radiologists when analyzing AI literature. Since AI methodologies in pediatric radiology research fundamentally differ from classical statistical analyses, an educational poster dedicated to interpreting pediatric radiology AI literature would assist in bridging this gap.

The purpose of this education exhibit is to provide a resource for pediatric radiologists that teaches a systematic approach for the interpretation of AI research publications. Knowledge delivery will be made by eLearning slide-based presentation. The major topics regarding AI literature will focus on: problem identification; data curation; data annotation, artificial intelligence models; validation and performance. Next, we review prior literature on important criteria for AI publications, including Checklist for Artificial Intelligence in Medical Imaging (CLAIM), the AI version of the Standards for Reporting of Diagnostic Accuracy Studies (STARD-AI), Transparent Report of Multivariable Prediction Model of Individual Prognosis and Diagnosis for AI (TRIPOD-AI) and Prediction Model Risk of Bias Assessment Tool for AI (PROBAST-AI). Lastly, we present these criteria in the context of pediatric AI research applications.
AI research methodology in pediatric radiology differs from classical research methodologies. Therefore, this education exhibit is intended to teach the systematic interpretation of the primary AI literature to pediatric radiologists in order to translate scientific knowledge into meaningful clinical information.
Methods & Materials:
Results:
Conclusions:
  • Rafful, Patricia  ( The Children's Hospital of Philadelphia Research Institute , Philadelphia , Pennsylvania , United States )
  • Alkhulaifat, Dana  ( The Children's Hospital of Philadelphia Research Institute , Philadelphia , Pennsylvania , United States )
  • Lopez Rippe, Julian  ( The Children's Hospital of Philadelphia Research Institute , Philadelphia , Pennsylvania , United States )
  • Khalkhali, Vahid  ( The Children's Hospital of Philadelphia Research Institute , Philadelphia , Pennsylvania , United States )
  • Welsh, Michael  ( The Children's Hospital of Philadelphia Research Institute , Philadelphia , Pennsylvania , United States )
  • Venkatakrishna, Shyam Sunder  ( The Children's Hospital of Philadelphia Research Institute , Philadelphia , Pennsylvania , United States )
  • Wieczkowski, Sydney  ( The Children's Hospital of Philadelphia Research Institute , Philadelphia , Pennsylvania , United States )
  • Reid, Janet  ( The Children's Hospital of Philadelphia Research Institute , Philadelphia , Pennsylvania , United States )
  • Sotardi, Susan  ( The Children's Hospital of Philadelphia Research Institute , Philadelphia , Pennsylvania , United States )
Session Info:

Posters - Educational

Informatics, Education, QI, or Healthcare Policy

SPR Posters - Educational

More abstracts on this topic:
Trends and Characteristics of Pediatric Radiology Research: 2006 - 2015

Lacroix Caroline, Shelmerdine Susan, Chavhan Govind

Data Science for Pediatric Radiologists: A Guide for Data Management in Artificial Intelligence Research

Alkhulaifat Dana, Rafful Patricia, Lopez Rippe Julian, Khalkhali Vahid, Welsh Michael, Wieczkowski Sydney, Reid Janet, Sotardi Susan

More abstracts from these authors:
Data Science for Pediatric Radiologists: A Guide for Data Management in Artificial Intelligence Research

Alkhulaifat Dana, Rafful Patricia, Lopez Rippe Julian, Khalkhali Vahid, Welsh Michael, Wieczkowski Sydney, Reid Janet, Sotardi Susan

Using Case-Based Learning to Teach Machine Learning in Pediatric Radiology

Alkhulaifat Dana, Rafful Patricia, Lopez Rippe Julian, Khalkhali Vahid, Welsh Michael, Wieczkowski Sydney, Reid Janet, Sotardi Susan

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Poster____EDU-044.pdf
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