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European Society of Paediatric Radiology Artificial Intelligence taskforce: a new taskforce for the digital age
Lene Bjerke Laborie1,2, Jaishree Naidoo3, Erika Pace4
1Department of Radiology, Section for Paediatrics, Haukeland University Hospital, Bergen, Norway.
Pediatric Radiology
|June 22, 2022
Summary
A new task force for artificial intelligence (AI) in pediatric radiology was established. It aims to provide education, foster collaboration, and guide future AI research in children's imaging.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Pediatric Radiology
Background:
- A dedicated task force for artificial intelligence (AI) in pediatric radiology was formed in 2021.
- The initiative stemmed from an international survey by the European Society of Pediatric Radiology (ESPR) assessing healthcare professionals' views on AI in pediatric imaging.
- Over 80% of ESPR respondents supported the task force's creation.
Purpose of the Study:
- To establish a dedicated task force for AI in pediatric radiology.
- To address healthcare professionals' opinions, expectations, and concerns regarding AI integration.
- To define key objectives for advancing AI in pediatric imaging.
Main Methods:
- Formation of an AI task force at the International Paediatric Radiology (IPR) meeting.
- Conducting an international survey of healthcare professionals' opinions on AI in pediatric radiology.
- Defining objectives including educational content, project brainstorming, and collaborative studies.
Main Results:
- The ESPR AI task force was successfully created in 2021.
- An international survey informed the task force's objectives, with strong support from ESPR respondents.
- Key objectives were defined, focusing on education, collaboration, and data-driven AI research.
Conclusions:
- The ESPR AI task force represents a significant step towards integrating AI in pediatric radiology.
- The task force is poised to provide educational resources and foster collaborative research.
- Future efforts will focus on data set collation, image de-identification, and multi-reader studies to advance AI applications in children's imaging.
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