ArticleJMIR AI2026
AI-Assisted Systematic Literature Review of the Economic Burden of Pneumococcal Disease: Development and Validation Study.
Article in JMIR AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Analysis of article screening and data extraction performance by an AI systematic literature review platform.Frontiers in artificial intelligence · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Automated systematic literature review (SLR) may reduce the workload and errors associated with manual review, enabling faster, up-to-date reviews even with increasing publication volumes. Large language models (LLMs) have demonstrated strong capabilities in understanding unstructured languages. However, few studies have explored the potential of a comprehensive LLM platform to streamline the entire SLR process from article screening to data extraction. Objective: This study aimed to investigate the feasibility of applying an LLM-based system to assist with SLR development. Methods: We developed the Intelligent Systematic Literature Review (ISLaR 2.0) platform, powered by an LLM, and applied it to a use case of the economic burden of pneumococcal disease (PD) literature. First, we established the inclusion and exclusion criteria for the SLR. Second, we defined data elements related to economic burden and domain knowledge, along with guidelines for applying these definitions. Finally, we used the criteria and data element specifications to develop LLM prompts for screening and data extraction. For data extraction, we identified relevant study characteristics and economic burden outcomes. We evaluated ISLaR 2.0's performance against a gold standard of 50 expert-curated PD articles, using standard metrics (accuracy, precision, recall, and F1-score). We also conducted a qualitative analysis to describe errors made by the system. Results: ISLaR 2.0 performed well in abstract and full-text screening (F1-scores of 86.27 for abstract screening and 87.18 for full-text screening) and data extraction from text (F1-scores of 92.83 for study details and 79.76 for economic burden outcomes). The F1-score for data extraction of tabular economic burden outcome data was 94.83. The qualitative analysis revealed 2 main challenges in extracting economic burden details: misclassification of cost categories and failure to extract relevant information. Conclusions: ISLaR 2.0 enabled efficient execution of an SLR regarding the economic burden of PD. The platform allowed users to flexibly define and modify criteria and data elements, supporting its use across a broad range of health research topics.
Indexed as
Identifiers
What OpenQuestion holds
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.