ArticleJournal of digital imaging2023
Natural Language Processing Model for Identifying Critical Findings-A Multi-Institutional Study.
Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- Data mining in pediatric radiology in the era of artificial intelligence.Pediatric radiology · 2026Review
- Portability of a text mining algorithm for detecting adverse drug reactions in electronic health records across diverse patient groups in two Dutch hospitals.PLOS digital health · 2026Article
- Artificial intelligence in pancreatitis: A narrative review on advancing precision diagnosis, prognosis, and therapeutic strategies.World journal of gastroenterology · 2025Review
- Automated Detection of Cancer-Suspicious Findings in Japanese Radiology Reports with Natural Language Processing: A Multicenter Study.Journal of imaging informatics in medicine · 2025Article
- In-Context Learning with Large Language Models: A Simple and Effective Approach to Improve Radiology Report Labeling.Healthcare informatics research · 2025Article
- Structured Transformation of Unstructured Prostate MRI Reports Using Large Language Models.Tomography (Ann Arbor, Mich.) · 2025Article
- Weakly supervised language models for automated extraction of critical findings from radiology reports.NPJ digital medicine · 2025Article
- Machine Learning and Natural Language Processing to Improve Classification of Atrial Septal Defects in Electronic Health Records.Birth defects research · 2025Article
- Comparison of natural language processing algorithms in assessing the importance of head computed tomography reports written in Japanese.Japanese journal of radiology · 2024Article
- Extracting patient lifestyle characteristics from Dutch clinical text with BERT models.BMC medical informatics and decision making · 2024Article
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
Abstract
Improving detection and follow-up of recommendations made in radiology reports is a critical unmet need. The long and unstructured nature of radiology reports limits the ability of clinicians to assimilate the full report and identify all the pertinent information for prioritizing the critical cases. We developed an automated NLP pipeline using a transformer-based ClinicalBERT
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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.