LitSense 2.0: AI-powered biomedical information retrieval with sentence and passage level knowledge discovery

作者：Lana Yeganova, Won Bae Kim, Shubo Tian, Donald C. Comeau, W. John Wilbur, Zhiyong Lu · 发表于：Nucleic Acids Research · 年份：2025 · DOI：10.1093/nar/gkaf417 · 被引用次数：7 · 研究领域：Topic Modeling、Biomedical Text Mining and Ontologies、Natural Language Processing Techniques

LitSense 2.0 (https://www.ncbi.nlm.nih.gov/research/litsense2/) is an advanced biomedical search system enhanced with dense vector semantic retrieval, designed for accessing literature on sentence and paragraph levels. It provides unified access to 38 million PubMed abstracts and 6.6 million full-length articles in the PubMed Central (PMC) Open Access subset, encompassing 1.4 billion sentences and ∼300 million paragraphs, and is updated weekly. Compared to PubMed and PMC, the primary platforms for biomedical information search, LitSense offers cross-platform functionality by searching seamlessly across both PubMed and PMC and returning relevant results at a more granular level. Building on the success of the original LitSense launched in 2018, LitSense 2.0 introduces two major enhancements. The first is the addition of paragraph-level search: users can now choose to search either against sentences or against paragraphs. The second is improved retrieval accuracy via a state-of-the-art biomedical text encoder, ensuring more reliable identification of relevant results across the entire biomedical literature.