Artificial Intelligence and Pediatrics: Synthetic Knowledge Synthesis

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.

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“…Another important indicator of the research state of a scientific field/sub-field is research funding [22]. Our analysis showed that 41.1% of papers are funded, notably more than in many other disciplines [18]; however, less than in a comparable sub-fieldnamely, the use of AI in pediatrics, where 47.4% of papers are funded [2]. The most prolific funding sponsor is the National Natural Science Foundation of China (n = 987), the…”

“…In the last couple of years, artificial intelligence (AI) has witnessed exponential growth in development, the rise of AI use, and increased public interest at different levels, from individual to organizational [1][2][3]. Modern AI utilizes machine learning and other advanced techniques to generate new knowledge, content, hypotheses, and even innovative ideas by identifying patterns and information usually found in extensive data-sized databases.…”

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