ArticleBioinformatics (Oxford, England)2023
MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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Who cites it
75 citing papers in PubMed.
- Development and Validation of Human-AI Collaborative Workflow in SNOMED CT Mapping of Bilingual Clinical Text.Journal of medical systems · 2026Article
- Benchmarking large language models for question answering on German clinical practice guidelines.BMJ health & care informatics · 2026Article
- An embedding-based framework enables statistical testing of gene-set function hypotheses inferred by large language models.Nature communications · 2026Article
- Effects of Model Choice, Corpus Context, and Post Hoc Correction on Layer-Level Embedding Degradation in Clinical Document Retrieval: Experimental Study.JMIR medical informatics · 2026Article
- EDEL: enhancing dense retrievers for curation of biomedical knowledge bases.Bioinformatics (Oxford, England) · 2026Article
- CellExLink: End-to-end cell-type recognition and normalization in biomedical text.PLoS computational biology · 2026Article
- PHO-Agents: A Large Language Model-Powered Multi-Agent System for Predicting Health Outcomes.medRxiv : the preprint server for health sciences · 2026Article
- Rare-Disease Diagnosis on the ZebraMap Multimodal Case Report Dataset: A Hybrid Pipeline with Grounded Explainability.Sensors (Basel, Switzerland) · 2026Article
- Open problems in ageing science: a roadmap for biogerontology.GeroScience · 2026Review
- A critical evaluation of generative query expansion on biomedical literature retrieval.Journal of the American Medical Informatics Association : JAMIA · 2026Article
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- Clinical Context Variables Collectively Rival Model Choice in Embedding-Based Retrieval: Multi-Corpus Benchmark Study.JMIR medical informatics · 2026Article
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- Large language models for structured cardiovascular data extraction: a foundation for scalable research and clinical applications.European heart journal. Digital health · 2026Article
- Retrieval-Augmented Generation for Medical Question Answering on a Heart Failure Dataset: Performance Analysis.JMIR formative research · 2026Article
- CancerLLM: a large language model in cancer domain.NPJ digital medicine · 2026Article
- Cell phenotypes in the biomedical literature: a systematic analysis and text mining corpus.bioRxiv : the preprint server for biology · 2026Article
- PlasRAG: comprehensive plasmid characterization and retrieval through sequence-text alignment.Genome biology · 2026Article
15 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
motivationInformation retrieval (IR) is essential in biomedical knowledge acquisition and clinical decision support. While recent progress has shown that language model encoders perform better semantic retrieval, training such models requires abundant query-article annotations that are difficult to obtain in biomedicine. As a result, most biomedical IR systems only conduct lexical matching. In response, we introduce MedCPT, a first-of-its-kind Contrastively Pre-trained Transformer model for zero-shot semantic IR in biomedicine.
resultsTo train MedCPT, we collected an unprecedented scale of 255 million user click logs from PubMed. With such data, we use contrastive learning to train a pair of closely integrated retriever and re-ranker. Experimental results show that MedCPT sets new state-of-the-art performance on six biomedical IR tasks, outperforming various baselines including much larger models, such as GPT-3-sized cpt-text-XL. In addition, MedCPT also generates better biomedical article and sentence representations for semantic evaluations. As such, MedCPT can be readily applied to various real-world biomedical IR tasks. AVAILABILITY AND IMPLEMENTATION: The MedCPT code and model are available at https://github.com/ncbi/MedCPT.
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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.