ABSTRACT
An exponential growth of literature about novel coronavirus disease 19 (COVID-19) has been observed in the last few months. This textual analysis of 5,780 publications extracted from the Web of Science, Medline, and Scopus databases was performed to explore the current research focuses and propose further research agenda. The Latent Dirichlet allocation was used for topic modeling. Regression analysis was conducted to examine country variations in the research focuses. Results indicated that publications were mainly contributed by the United States, China, and European countries. Guidelines for emergency care and surgical, viral pathogenesis, and global responses in the COVID-19 pandemic were the most common topics. There was variation in the research approaches to mitigate COVID-19 problems in countries with different income and transmission levels. Findings highlighted the need for global research collaboration among high- and low/middle-income countries in the different stages of prevention and control the pandemic.
ABSTRACT
This review paper presents a framework to evaluate the artificial intelligence (AI) readiness for the healthcare sector in developing countries: a combination of adequate technical or technological expertise, financial sustainability, and socio-political commitment embedded in a healthy psycho-cultural context could bring about the smooth transitioning toward an AI-powered healthcare sector. Taking the Vietnamese healthcare sector as a case study, this paper attempts to clarify the negative and positive influencers. With only about 1500 publications about AI from 1998 to 2017 according to the latest Elsevier AI report, Vietnamese physicians are still capable of applying the state-of-the-art AI techniques in their research. However, a deeper look at the funding sources suggests a lack of socio-political commitment, hence the financial sustainability, to advance the field. The AI readiness in Vietnam's healthcare also suffers from the unprepared information infrastructure-using text mining for the official annual reports from 2012 to 2016 of the Ministry of Health, the paper found that the frequency of the word "database" actually decreases from 2012 to 2016, and the word has a high probability to accompany words such as "lacking", "standardizing", "inefficient", and "inaccurate." Finally, manifestations of psycho-cultural elements such as the public's mistaken views on AI or the non-transparent, inflexible and redundant of Vietnamese organizational structures can impede the transition to an AI-powered healthcare sector.
