ArticleMedical review (2021)2023
Applications of cutting-edge artificial intelligence technologies in biomedical literature and document mining.
Article in Medical review (2021), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- A FAIR gold-standard benchmark for geographic entity recognition in cancer genomics literature for biomedical NLP.Bioinformatics advances · 2026Article
- The dawn of a new era: can machine learning and large language models reshape QSP modeling?Journal of pharmacokinetics and pharmacodynamics · 2025Review
- Artificial intelligence and bioinformatics: a journey from traditional techniques to smart approaches.Gastroenterology and hepatology from bed to bench · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The biomedical literature is a vast and invaluable resource for biomedical research. Integrating knowledge from the literature with biomedical data can help biological studies and the clinical decision-making process. Efforts have been made to gather information from the biomedical literature and create biomedical knowledge bases, such as KEGG and Reactome. However, manual curation remains the primary method to retrieve accurate biomedical entities and relationships. Manual curation becomes increasingly challenging and costly as the volume of biomedical publications quickly grows. Fortunately, recent advancements in Artificial Intelligence (AI) technologies offer the potential to automate the process of curating, updating, and integrating knowledge from the literature. Herein, we highlight the AI capabilities to aid in mining knowledge and building the knowledge base from the biomedical literature.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.