Comparison of performances of top emergency medicine journals in terms of COVID-19 publications in 2020

Artificial intelligence in emergency medicine. A systematic literature review

Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies.

METHODS

The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms.

FINDINGS AND DISCUSSION

The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction.

CONCLUSION

Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.

Global research trends in COVID-19 with MRI and PET/CT: a scoping review with bibliometric and network analyses

OBJECTIVE

To identify and evaluate the indexed studies that allow us to understand the implications of imaging studies in MRI and PET/CT related to COVID-19 research.

METHODS

Scoping review. Articles in PubMed, Scopus, and Web of Science (WoS) were scanned from 2019 to 2021 with COVID-19, MRI, and PET-CT as keywords. EndNote software and manual checking removed the duplicated references. Our assessment includes citation, bibliometric, keyword network, and statistical analyses using descriptive statistics and correlations. Highlighted variables were publication year, country, journals, and authorship.

RESULTS

Only 326 papers were included. The most cited article reached 669 cites; this number represented 21.71% of 3081 citations. The top-15 cited authors received 1787 citations, which represented 58% of the total cites. These authors had affiliations from ten countries (Belgium, China, France, Italy, Japan, Spain, Sweden, Turkey, United Kingdom (UK), and the USA). The top-30 journals were cited 2762 times, representing 89.65% of the total cites. Only five journals were cited more than 100 times; Int J Infect Dis had the most significant number of citations (674). Some of the unexpected keywords were encephalitis, stroke, microbleeds, myocarditis.

CONCLUSION

COVID-19 pandemic is still spreading worldwide, and the knowledge about its different facets continues advancing. MRI and PET/CT are being used in more than 50% of the selected studies; research trends span seven categories, no only the diagnostic but others like socio-economic impact and pathogenesis Developed countries had an advantage by having hospitals with more resources, including MRI and PET/CT facilities in the same institution to supplement basic assessment in patients with COVID-19.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40336-021-00460-x.