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Gaps and Opportunities of Artificial Intelligence Applications for Pediatric Oncology in European Research: A Systematic Review of Reviews and a Bibliometric Analysis

Journal

FRONTIERS IN ONCOLOGY
Volume 12, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fonc.2022.905770

Keywords

artificial intelligence; pediatric oncology; childhood cancer; machine learning; deep learning; CNS tumors

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The application of artificial intelligence systems in various fields has increased in recent years, thanks to the availability of more powerful and cost-effective computing resources. Although there are many promising applications of AI in pediatric oncology and other medical fields, its use is still in its early stages. This paper provides an overview of the current state of AI application in pediatric oncology through a systematic review of systematic reviews, as well as an analysis of trends in Europe based on publications by European authors. The findings show that while there is significant scientific production in Europe, there is still a lack of robust evidence in the domains investigated by systematic reviews. The use of AI in pediatric oncology is developing rapidly, but there are still gaps and challenges that need to be addressed to validate its use in clinical practice.
The application of artificial intelligence (AI) systems is emerging in many fields in recent years, due to the increased computing power available at lower cost. Although its applications in various branches of medicine, such as pediatric oncology, are many and promising, its use is still in an embryonic stage. The aim of this paper is to provide an overview of the state of the art regarding the AI application in pediatric oncology, through a systematic review of systematic reviews, and to analyze current trends in Europe, through a bibliometric analysis of publications written by European authors. Among 330 records found, 25 were included in the systematic review. All papers have been published since 2017, demonstrating only recent attention to this field. The total number of studies included in the selected reviews was 674, with a third including an author with a European affiliation. In bibliometric analysis, 304 out of the 978 records found were included. Similarly, the number of publications began to dramatically increase from 2017. Most explored AI applications regard the use of diagnostic images, particularly radiomics, as well as the group of neoplasms most involved are the central nervous system tumors. No evidence was found regarding the use of AI for process mining, clinical pathway modeling, or computer interpreted guidelines to improve the healthcare process. No robust evidence is yet available in any of the domains investigated by systematic reviews. However, the scientific production in Europe is significant and consistent with the topics covered in systematic reviews at the global level. The use of AI in pediatric oncology is developing rapidly with promising results, but numerous gaps and challenges persist to validate its utilization in clinical practice. An important limitation is the need for large datasets for training algorithms, calling for international collaborative studies.

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