Artificial intelligence and pediatrics

synthetic knowledge synthesis

Jernej Završnik (Author), Peter Kokol (Author), Bojan Žlahtič (Author), Helena Blažun (Author)

Abstract

The first publication on the use of artificial intelligence (AI) in pediatrics dates back to 1984. Since then, research on AI in pediatrics has become much more popular, and the number of publications has largely increased. Consequently, a need for a holistic research landscape enabling researchers and other interested parties to gain insights into the use of AI in pediatrics has arisen. To fill this gap, a novel methodology, synthetic knowledge synthesis (SKS), was applied. Using SKS, we identified the most prolific countries, institutions, source titles, funding agencies, and research themes and the most frequently used AI algorithms and their applications in pediatrics. The corpus was extracted from the Scopus (Elsevier, The Netherlands) bibliographic database and analyzed using VOSViewer, version 1.6.20. Done An exponential growth in the literature was observed in the last decade. The United States, China, and Canada were the most productive countries. Deep learning was the most used machine learning algorithm and classification, and natural language processing was the most popular AI approach. Pneumonia, epilepsy, and asthma were the most targeted pediatric diagnoses, and prediction and clinical decision making were the most frequent applications.

Keywords

pediatrija;umetna inteligenca;bibliometrika;strojno učenje;pediatrics;artificial intelligence;synthetic knowledge synthesis;bibliometrics;machine learning;

Data

Language:	English
Year of publishing:	2024
Typology:	1.02 - Review Article
Organization:	UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher:	MDPI
UDC:	004.5
COBISS:	182783747
ISSN:	2079-9292
Views:	0
Downloads:	3
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	pediatrija;umetna inteligenca;bibliometrika;strojno učenje;
Type (COBISS):	Article
Pages:	14 str.
Volume:	ǂVol. ǂ13
Issue:	ǂiss. ǂ3, [article no.] 512
Chronology:	Jan. 2024
DOI:	10.3390/electronics13030512
ID:	26705082