LitCovid in 2022: an information resource for the COVID-19 literature
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Abstract (arXiv)
LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), first launched in February 2020, is a first-of-its-kind literature hub for tracking up-to-date published research on COVID-19. The number of articles in LitCovid has increased from 55,000 to ~300,000 over the past two and half years, with a consistent growth rate of ~10,000 articles per month. In addition to the rapid literature growth, the COVID-19 pandemic has evolved dramatically. For instance, the Omicron variant has now accounted for over 98% of new infections in the U.S. In response to the continuing evolution of the COVID-19 pandemic, this article describes significant updates to LitCovid over the last two years. First, we introduced the Long Covid collection consisting of the articles on COVID-19 survivors experiencing ongoing multisystemic symptoms, including respiratory issues, cardiovascular disease, cognitive impairment, and profound fatigue. Second, we provided new annotations on the latest COVID-19 strains and vaccines mentioned in the literature. Third, we improved several existing features with more accurate machine learning algorithms for annotating topics and classifying articles relevant to COVID-19. LitCovid has been widely used with millions of accesses by users worldwide on various information needs and continues to play a critical role in collecting, curating, and standardizing the latest knowledge on the COVID-19 literature.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Sep 2022.
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Citing papers per year: 2023: 3; 2024: 2; 2025: 2.
7 papers.
- 2023Benchmarking large language models for biomedical natural language processing applications and recommendationscited by 78
- 2023PubMed and Beyond: Biomedical Literature Search in the Age of Artificial Intelligencecited by 16
- 2023Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphscited by 4
- 2025Can Frontier LLMs Replace Annotators in Biomedical Text Mining? Analyzing Challenges and Exploring Solutionscited by 1
- 2024Advancing Chinese biomedical text mining with community challengescited by 0
- 2024Bag of Lies: Robustness in Continuous Pre-training BERTcited by 0
- 2025Academic case reports lack diversity: Assessing the presence and diversity of sociodemographic and behavioral factors related to Post COVID-19 Conditioncited by 0
- 2025Can Frontier LLMs Replace Annotators in Biomedical Text Mining? Analyzing Challenges and Exploring Solutions5 Mar 2025
- 2025Academic case reports lack diversity: Assessing the presence and diversity of sociodemographic and behavioral factors related to Post COVID-19 Condition21 Jan 2025
- 2024Bag of Lies: Robustness in Continuous Pre-training BERT14 Jun 2024
- 2024Advancing Chinese biomedical text mining with community challenges7 Mar 2024
- 2023PubMed and Beyond: Biomedical Literature Search in the Age of Artificial Intelligence18 Jul 2023
- 2023Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphs17 Jul 2023
- 2023Benchmarking large language models for biomedical natural language processing applications and recommendations10 May 2023