Publications on COVID-19 and artificial intelligence: trends and lessons
| Title: | Publications on COVID-19 and artificial intelligence: trends and lessons |
|---|---|
| Authors: | Yeong Jae Kim, Yang Liu, Youngeun Kim, Ho Won Jang |
| Source: | Science Editing, Vol 11, Iss 2, Pp 142-148 (2024) |
| Publisher Information: | Korean Council of Science Editors, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Science (General) |
| Subject Terms: | publication trends, covid-19, artificial intelligence, journal impact factor, journal citation, Science (General), Q1-390 |
| More Details: | Purpose This study investigates shifts in scientific research focus, particularly the decline in COVID-19-related research and the rapid growth of artificial intelligence (AI) publications. Methods We analyzed publication data from the Web of Science, comparing yearly publication counts for COVID-19 and AI research. The study also tracked changes in the impact factors of leading journals like Science and Nature, alongside those of top AI journals over the past decade. Additionally, we reviewed the top 10 most cited articles in 2021 from Science and Nature and the most influential AI publications from the past five years according to Google Scholar. The impact trends of the top 100 AI journals in computer science were also explored. Results The analysis reveals a noticeable decline in COVID-19 related publications as the pandemic urgency diminishes, contrasted with the continued rapid growth of AI research. Impact factors for prestigious journals have shifted, with AI journals increasingly dominating the academic landscape. The review of top-cited articles further emphasizes these trends. Conclusion Our findings indicate a significant shift in research priorities, with AI emerging as a dominant field poised to address future challenges, reflecting the evolving focus of the scientific community. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2288-8063 2288-7474 |
| Relation: | http://escienceediting.org/upload/pdf/kcse-338.pdf; https://doaj.org/toc/2288-8063; https://doaj.org/toc/2288-7474 |
| DOI: | 10.6087/kcse.338 |
| Access URL: | https://doaj.org/article/33f74c7823a94168971d40524dfa9be6 |
| Accession Number: | edsdoj.33f74c7823a94168971d40524dfa9be6 |
| Database: | Directory of Open Access Journals |
| ISSN: | 22888063 22887474 |
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| DOI: | 10.6087/kcse.338 |
| Published in: | Science Editing |
| Language: | English |