ArticlePLOS digital health2025
Using large language models to extract information from pediatric clinical reports.
Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Iterative Development of an AI-Assisted Data Extraction Tool for Literature Synthesis in Oncology.Cureus · 2026Article
- Extracting adverse event nature, severity, timelines and resulting interventions from clinical notes of patients receiving CAR-T cell therapy using large language models.PLOS digital health · 2026Article
- Adaptive Fast-Slow Large Language Model Framework for Multidimensional Classification of Prenatal Ultrasound Reports: Comparative Study.Journal of medical Internet research · 2026Article
- Extracting adverse event nature, severity, timelines and resulting interventions from clinical notes of patients receiving CAR-T therapy using large language models.medRxiv : the preprint server for health sciences · 2026Article
- Leveraging Large Language Models to Extract Prognostic Pathology Features in Ewing Sarcoma.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most medical documentation, including clinical reports, exists in unstructured formats, which hinder efficient data analysis and integration into decision-making systems for patient care and research. Both fields could profit significantly from a reliable automatic analysis of these documents. Current methods for data extraction from these documents are labor-intensive and inflexible. Large Language Models (LLMs) offer a promising alternative for transforming unstructured medical documents into structured data in a flexible manner. This study assesses the performance of large language models (LLMs) in extracting structured data from pediatric clinical reports. Nine different LLMs were assessed. The results demonstrate that both commercial and open-source LLMs can achieve high accuracy in identifying patient-specific information, with top-performing models achieving over 90% accuracy in key tasks.
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.