ArticleNPJ digital medicine2025
The use of large language models in detecting Chinese ultrasound report errors.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Human-AI collaboration using GPT-5 for MR grading of endolymphatic hydrops in Ménière's disease.European radiology · 2026Article
- From zero-shot to fine-tuning: optimize large language models for error detection of ultrasound reports.Insights into imaging · 2026Article
- Error Detection and Correction in Chinese Radiology Reports Using Large Language Models: Real-World Clinical Validation Study.Journal of medical Internet research · 2026Article
- Multimodal Large Language Models for Breast Ultrasound Report Auditing: Workflow-Error Detection, False-Positive Burden, and Limits of Key-Image Interpretation.Journal of imaging informatics in medicine · 2026Article
- Error Detection in Emergency Radiology Reports Using a Large Language Model: Multistage Evaluation Study.Journal of medical Internet research · 2026Article
- ChatGPT-5 versus other mainstream large language models in core diabetic retinopathy patient queries.Frontiers in cell and developmental biology · 2026Article
- Exploring the use of large language models for classification, clinical interpretation, and treatment recommendation in breast tumor patient records.Scientific reports · 2025Article
- Using ChatGPT to assist in judging the indications for emergency ultrasound: an innovative exploration of optimizing medical resource allocation.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This retrospective study evaluated the efficacy of large language models (LLMs) in improving the accuracy of Chinese ultrasound reports. Data from three hospitals (January-April 2024) including 400 reports with 243 errors across six categories were analyzed. Three GPT versions and Claude 3.5 Sonnet were tested in zero-shot settings, with the top two models further assessed in few-shot scenarios. Six radiologists of varying experience levels performed error detection on a randomly selected test set. In zero-shot setting, Claude 3.5 Sonnet and GPT-4o achieved the highest error detection rates (52.3% and 41.2%, respectively). In few-shot, Claude 3.5 Sonnet outperformed senior and resident radiologists, while GPT-4o excelled in spelling error detection. LLMs processed reports faster than the quickest radiologist (Claude 3.5 Sonnet: 13.2 s, GPT-4o: 15.0 s, radiologist: 42.0 s per report). This study demonstrates the potential of LLMs to enhance ultrasound report accuracy, outperforming human experts in certain aspects.
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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.