Academic impact and on-line attention of papers on artificial intelligence in health field: Bibliometric and altmetric analysis

Abstract

Objective: Artificial intelligence, as an interdisciplinary field, presents its literature in several areas of knowledge, and its applications offers important contributions for the scientific fields that it dedicates. This study aim is to analyze the scientific production on artificial intelligence in open access scientific articles in the health field and understand its indicators of impact. Methods: It is a descriptive, exploratory and quantitative research, that uses bibliometric and altmetric techniques in complementarity with the aim of ascertaining academic impact through citations data and the on-line attention through the mentions found in the sources of the Social Web. The production data and its metrics were obtained in consultation in Dimensions; and altmetric data retrieved via Altmetric Explorer. Results: In total, 3,121 articles published between 2014 and 2019 were analyzed. The temporal distribution shows increase, with 2019 showing the highest concentration of articles. The academic impact was considered moderate, 70.81% have citation data and, for the visibility, 61.93% presented altmetric data regarding the mentions in the sources of the Social Web. Citations are frequent in old articles and on-line attention is greater for recent publications. Social media as sources with a greater number of mentions, followed by news portals and blogs. Conclusions: It is considered that, by complementing traditional metric studies, such as bibliometrics and citations, altmetrics and its alternative indicators enable a broader understanding of the real impact caused by scientific productions.