biostudies-literatureUnknown8(1)Yan YThis 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 qNPJ digital medicine66https://www.ebi.ac.uk/biostudies/studies/S-EPMC11775253biostudies-literatureThe use of large language models in detecting Chinese ultrasound report errors.PMC11775253Zhou LWang VYYao JZheng YYang JXu DDu YJiang TYan YFeng BZhou YChen CJin ZWang KSui LChen XPan QLiang PfalseThe use of large language models in detecting Chinese ultrasound report errors.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 q2025-01-01T00:00:00Z2025 Jan2025-04-03T23:26:40.764Z2025-04-03T23:26:40.764ZS-EPMC117752533987580010.1038/s41746-025-01468-7