ArticleSystematic reviews2024
Natural language processing (NLP) to facilitate abstract review in medical research: the application of BioBERT to exploring the 20-year use of NLP in medical research.
Article in Systematic reviews, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- [Empowering narrative medicine with artificial intelligence: a new perspective in pediatric medical education].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Pooled it
- A comparative study of screening performance between abstrackr and GPT models: Systematic review and contextual analysis.BMC medical informatics and decision making · 2025Pooled it
- Natural language processing in dermatology: A systematic literature review and state of the art.Journal of the European Academy of Dermatology and Venereology : JEADV · 2024Pooled it
- Techniques, Performance, and Feasibility of Natural Language Processing for Abstract Screening in Evidence Synthesis: A Systematic Review.Campbell systematic reviews · 2026Review
- Navigating the Artificial Intelligence Revolution in Clinical Neurology: A New Multidisciplinary Task Force Within the European Academy of Neurology.European journal of neurology · 2026Article
- Enhanced semantic classification of microbiome sample origins using large language models (LLMs).GigaScience · 2026Article
- Machine Learning-Based Identification of Natural History Studies in Rare Diseases: A Step toward Understanding Disease Development and Outcome.Journal of rare diseases (Berlin, Germany) · 2025Article
- GlycoSiteMiner: an ML/AI-assisted literature mining-based pipeline for extracting glycosylation sites from PubMed abstracts.Glycobiology · 2025Article
- Nested Named Entity Recognition using Multilayer BERT-based Model: Notebook for the BioASQ Lab at CLEF 2024.CEUR workshop proceedings. · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAbstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical research, to demonstrate the performance of this automated abstract review model.
methodsScanning PubMed, Embase, PsycINFO, and CINAHL databases, we identified 22,294 with a final selection of 12,817 English abstracts published between 2000 and 2021. We invented a manual classification of medical fields, three variables, i.e., the context of use (COU), text source (TS), and primary research field (PRF). A training dataset was developed after reviewing 485 abstracts. We used a language model called Bidirectional Encoder Representations from Transformers to classify the abstracts. To evaluate the performance of the trained models, we report a micro f1-score and accuracy.
resultsThe trained models' micro f1-score for classifying abstracts, into three variables were 77.35% for COU, 76.24% for TS, and 85.64% for PRF. The average annual growth rate (AAGR) of the publications was 20.99% between 2000 and 2020 (72.01 articles (95% CI: 56.80-78.30) yearly increase), with 81.76% of the abstracts published between 2010 and 2020. Studies on neoplasms constituted 27.66% of the entire corpus with an AAGR of 42.41%, followed by studies on mental conditions (AAGR = 39.28%). While electronic health or medical records comprised the highest proportion of text sources (57.12%), omics databases had the highest growth among all text sources with an AAGR of 65.08%. The most common NLP application was clinical decision support (25.45%).
conclusionsBioBERT showed an acceptable performance in the abstract review. If future research shows the high performance of this language model, it can reliably replace manual abstract reviews.
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Registered trials
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.