ArticleNature biotechnology2018
How user intelligence is improving PubMed.
Article in Nature biotechnology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements in Herpes Zoster Diagnosis, Treatment, and Management: Systematic Review of Artificial Intelligence Applications.Journal of medical Internet research · 2025Pooled it
- Automated literature surveillance in anesthesiology: an open, customizable dashboard.Brazilian journal of anesthesiology (Elsevier) · 2026Article
- Tutorial: guidance on the use of large language models for medical research.Nature protocols · 2026Review
- Optimising pain identification in resource-limited emergency departments using transfer learning and fine-tuned language models.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Exploring Anti-Aging Literature viaArXiv · 2026Article
- Exploring Anti-Aging Literature viaAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- LitSense 2.0: AI-powered biomedical information retrieval with sentence and passage level knowledge discovery.Nucleic acids research · 2025Article
- A large language model framework for literature-based disease-gene association prediction.Briefings in bioinformatics · 2024Article
- BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2024Article
- PubMed Computed Authors in 2024: an open resource of disambiguated author names in biomedical literature.Bioinformatics (Oxford, England) · 2024Article
- Progress and opportunities of foundation models in bioinformatics.Briefings in bioinformatics · 2024Review
- Bridging the gap in author names: building an enhanced author name dataset for biomedical literature system.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- NeighBERT: Medical Entity Linking Using Relation-Induced Dense Retrieval.Journal of healthcare informatics research · 2024Article
- Harnessing PubMed User Query Logs for Post Hoc Explanations of Recommended Similar Articles.ArXiv · 2024Article
- PubMed and beyond: biomedical literature search in the age of artificial intelligence.EBioMedicine · 2024Review
- Article
- Large-Scale Biomedical Relation Extraction Across Diverse Relation Types: Model Development and Usability Study on COVID-19.Journal of medical Internet research · 2023Article
- PubMed's core clinical journals filter: redesigned for contemporary clinical impact and utility.Journal of the Medical Library Association : JMLA · 2023Article
- A Systematic Review and Meta-Analysis Comparing the Diagnostic Accuracy Tests of COVID-19.Diagnostics (Basel, Switzerland) · 2023Review
- A study of deep active learning methods to reduce labelling efforts in biomedical relation extraction.PloS one · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
PubMed is a widely used search engine for biomedical literature. It is developed and maintained by the US National Library of Medicine/National Center for Biotechnology Information and is visited daily by millions of users around the world. For decades, PubMed has used advanced artificial intelligence technologies that extract patterns of collective user activity, such as machine learning and natural language processing, to inform the algorithmic changes that ultimately improve a user's search experience. Although these efforts have led to objective improvements in search quality, the technical underpinnings remain largely invisible and go largely unnoticed by most users. Here we describe how these 'under-the-hood' techniques work within PubMed and report how their effectiveness and usage is assessed in real-world scenarios. In doing so, we hope to increase the transparency of the PubMed system and enable users to make more effective use of the search engine. We also identify open challenges and new opportunities for computational researchers to explore the potential of future improvements.
Identifiers
30272675What 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.