Artificial intelligence in healthcare: A bibliometric analysis

ABSTRACT

Background The implementation of artificial intelligence technology in health care improves disease prediction, classification, and diagnosis, benefiting both patients and healthcare providers. a surge in artificial intelligence popularity, owing largely to an enormous increase in computational capabilities and an even greater increase in data generation. The study's purpose was to conduct a bibliometric analysis on healthcare-related artificial intelligence research from the years 2000 to 2021. Methods The Scopus dataset has been used to find and retrieve all existing and referenced healthcare-related artificial intelligence research published in English. Based on bibliometric indicators, the rate of publication growth, the subject area, and the top active countries, institutions, journals, and funding sponsors were analyzed. Results The search identified non-duplicated 5,019 papers. During the years 2000 to 2009, there were fewer publications, but they increased in the subsequent years. Moreover, research released after 2012 constitutes 88.88% of the total publications. Overall, 96.85% of the included studies have been published in 9 countries. About 41.84% of the studies included were from the US. The technology keywords that appeared most were "Machine Learning”, "Electronic health records”, and "Natural language processing”. Furthermore, Covid-19, Diabetes, Mental Health, Asthma, Dementia, and Cancer are some of the disease-related keywords that appeared frequently in healthcare-related artificial intelligence research. Conclusions The study carried out a thorough bibliometric study on healthcare-related artificial intelligence research, which will help researchers, legislators, and practitioners understand the field's growth and the prerequisites for responsible use of artificial intelligence technology within the healthcare system.