Automated indexing in MEDLINE and the Medical Text Indexer (MTI), 2000–2025: a scoping review
DOI:
https://doi.org/10.5195/jmla.2026.2406Keywords:
Automated indexing, Algorithmic indexing, Medical Text Indexer (MTI), Medical Subject Headings (MeSH), PubMed/MEDLINE Searching, National Library of Medicine (NLM), Neural Network, Search FiltersAbstract
Objectives: To synthesize and map literature on automated indexing of the biomedical literature, with a focus on the Medical Text Indexer (MTI) at the National Library of Medicine (NLM). We review the drivers, benefits, and challenges of automated indexing, evolution of the MTI from 2000-2025, and impacts on information retrieval in MEDLINE.
Methods: We conducted a scoping review following the JBI Manual for Evidence Synthesis and reported findings using PRISMA-ScR and PRISMA-S. We searched several bibliographic databases, key journals, conference proceedings, and grey literature sources, with no restrictions on language or study design. Eligible publications were published 2000-2025 and focused on MTI development. Screening, data charting, and thematic analysis were conducted by multiple reviewers.
Results: We included 64 publications, with most originating from the United States (n=53, 83%) and five from Canada (8%). Study methods included evaluation or comparative studies (65%), qualitative descriptions (25%), and mixed methods (11%). MTI evolved from a rules-based recommendation tool in 2002 to the neural network–based MTIX in 2024. Despite numerous enhancements to the MTI, human curation remains necessary for approximately one-third of records to correct inaccuracies, capture missed concepts, and address errors arising from figurative language or algorithmic biases.
Conclusions: This review synthesizes twenty-five years of MTI research (2000–2025). Despite reduced indexing times and a markedly improved algorithm, the MTIX has not yet achieved full equivalence to human indexing. Our findings suggest searchers should watch for algorithmic ambiguities in their MEDLINE searching and adapt accordingly. Health sciences librarians should work with stakeholders, including authors, to shape future algorithmic indexing methods, outputs, evaluation and research.
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