PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical
  Knowledge
Chih-Hsuan Wei Alexis Allot Po-Ting Lai Robert Leaman Shubo Tian Ling Luo Qiao Jin Zhizheng Wang Qingyu Chen Zhiyong Lu
PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a
biomedical literature resource using state-of-the-art AI techniques to offer
semantic and relation searches for key concepts like proteins, genetic
variants, diseases, and chemicals. It currently provides over one billion
entity and relation annotations across approximately 36 million PubMed
abstracts and 6 million full-text articles from the PMC open access subset,
updated weekly. PubTator 3.0's online interface and API utilize these
precomputed entity relations and synonyms to provide advanced search
capabilities and enable large-scale analyses, streamlining many complex
information needs. We showcase the retrieval quality of PubTator 3.0 using a
series of entity pair queries, demonstrating that PubTator 3.0 retrieves a
greater number of articles than either PubMed or Google Scholar, with higher
precision in the top 20 results. We further show that integrating ChatGPT
(GPT-4) with PubTator APIs dramatically improves the factuality and
verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive
set of features and tools that allow researchers to navigate the ever-expanding
wealth of biomedical literature, expediting research and unlocking valuable
insights for scientific discovery.