PubMed and Beyond: Biomedical Literature Search in the Age of Artificial Intelligence

Abstract

Biomedical research yields a wealth of information, much of which is only accessible through the literature. Consequently, literature search is an essential tool for building on prior knowledge in clinical and biomedical research. Although recent improvements in artificial intelligence have expanded functionality beyond keyword-based search, these advances may be unfamiliar to clinicians and researchers. In response, we present a survey of literature search tools tailored to both general and specific information needs in biomedicine, with the objective of helping readers efficiently fulfill their information needs. We first examine the widely used PubMed search engine, discussing recent improvements and continued challenges. We then describe literature search tools catering to five specific information needs: 1. Identifying high-quality clinical research for evidence-based medicine. 2. Retrieving gene-related information for precision medicine and genomics. 3. Searching by meaning, including natural language questions. 4. Locating related articles with literature recommendation. 5. Mining literature to discover associations between concepts such as diseases and genetic variants. Additionally, we cover practical considerations and best practices for choosing and using these tools. Finally, we provide a perspective on the future of literature search engines, considering recent breakthroughs in large language models such as ChatGPT. In summary, our survey provides a comprehensive view of biomedical literature search functionalities with 36 publicly available tools.

27 pages, 6 figures, 36 tools

References in corpus (6)

• A Survey of Large Language Models
• Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health
• Matching Patients to Clinical Trials with Large Language Models
• GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information
• Biomedical Question Answering: A Survey of Approaches and Challenges
• Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMs

Cited by in corpus (4)

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