Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study

Abstract

The increasing application of Artificial Intelligence (AI) in health and medicine has attracted a great deal of research interest in recent decades. This study aims to provide a global and historical picture of research concerning AI in health and medicine. A total of 27,451 papers that were published between 1977 and 2018 (84.6% were dated 2008⁻2018) were retrieved from the Web of Science platform. The descriptive analysis examined the publication volume, and authors and countries collaboration. A global network of authors' keywords and content analysis of related scientific literature highlighted major techniques, including Robotic, Machine learning, Artificial neural network, Artificial intelligence, Natural language process, and their most frequent applications in Clinical Prediction and Treatment. The number of cancer-related publications was the highest, followed by Heart Diseases and Stroke, Vision impairment, Alzheimer's, and Depression. Moreover, the shortage in the research of AI application to some high burden diseases suggests future directions in AI research. This study offers a first and comprehensive picture of the global efforts directed towards this increasingly important and prolific field of research and suggests the development of global and national protocols and regulations on the justification and adaptation of medical AI products.

Keywords: AI ethics; artificial intelligence; bibliometric analysis; global; health; mapping; medicine.

Figure 1

Selection of papers in the Web of Science database.

Figure 2

The number of papers by year in the database, 1977–2017.

Figure 3

The number of papers since the year 1977 (estimated and observed). The dotted lines (corresponding in term of color to the two solid lines) represent the 95% confidence interval.

Figure 4

The global network of coauthors.

Figure 5

The global network of the 93 countries (at least five papers). Note: Figure 5 visually represents the proportional contributions of each country, including their collaborations with one another. The size of each country node represents this proportional contribution of articles to the data set. Attribution of papers to a country was based on the institutional affiliation of the lead author. The length of the lines was automatically generated and was based on the strength of the collaboration between two countries.

Figure 6

The co-occurrence of authors’ keywords. Figure 6 presents the prevalence of 568 keywords appearing in our search results using the Web of Science. The thickness of lines is an indication of the strength of the relationship between keywords relative to the others. The strength of these relationships was determined by the frequency with which they appeared together in published articles. Their inclusion into specific thematic groups was based on their clustering with a certain constellation of terms. The position of a keyword within this constellation represents how interrelated and frequent its co-occurrence was with other terms.