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Artificial Intelligence in Health Care: Bibliometric Analysis.

Yuqi Guo1, Zhichao Hao2, Shichong Zhao3

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|July 30, 2020
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Summary
This summary is machine-generated.

The volume of health care artificial intelligence (AI) research is rapidly expanding. This bibliometric analysis reveals key trends, publication growth, and emerging research hotspots in AI for healthcare, highlighting areas for future clinical application.

Keywords:
artificial intelligencebibliometric analysishealth caremachine learningneural networkstelehealth

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Area of Science:

  • Bibliometrics and scientometrics
  • Artificial Intelligence in Healthcare
  • Health Informatics

Background:

  • The field of artificial intelligence (AI) is a significant driver of advancement in healthcare.
  • There is a rapidly growing body of literature on AI applications within the healthcare sector.
  • Understanding the trajectory of this research is crucial for stakeholders in healthcare and AI.

Purpose of the Study:

  • To conduct a dynamic and longitudinal bibliometric analysis of publications related to artificial intelligence in healthcare.
  • To identify trends in publication output, research characteristics, and emerging hotspots in healthcare AI research.
  • To provide insights for researchers, policymakers, and practitioners regarding the development and implications of AI in healthcare.

Main Methods:

  • A comprehensive search of the Web of Science database was performed for English-language AI-related healthcare papers published up to December 2019.
  • Bibliometric indicators were used to develop a search strategy, screening titles, abstracts, and full texts for eligibility.
  • HistCite software was employed to analyze publication growth rates, research activity characteristics, publication patterns, and research hotspot tendencies.

Main Results:

  • A total of 1473 publications were included in the analysis from an initial search of 5235 hits.
  • Publication output demonstrated an average annual growth of 17.02% since 1995, with a significant acceleration to 45.15% between 2014 and 2019.
  • Key research areas include cancer, depression, Alzheimer's disease, heart failure, and diabetes, with artificial neural networks, support vector machines, and convolutional neural networks showing the highest impact. Nucleosides, convolutional neural networks, and tumor markers were identified as persistent research hotspots.

Conclusions:

  • This bibliometric analysis offers a comprehensive overview of AI research in healthcare, aiding stakeholders in understanding research development and practical implications.
  • The findings highlight the increasing momentum and focus areas within healthcare AI research.
  • Future research should prioritize bridging the gap between AI healthcare research and its direct clinical applications.