From zero-shot to fine-tuning: optimize large language models for error detection of ultrasound reports
High workload and inconsistent quality of ultrasound report writing may lead to diagnostic errors. This study aims to ascertain whether fine-tuned open-source large language models (LLMs) can achieve promising performance for automated quality control of Chinese ultrasound reports, when compared to proprietary LLMs. This retrospective, multi-center study included a multi-subspecialty dataset of 1800 Chinese ultrasound reports, comprising 1500 quality-controlled reports injected artificially with six predefined error types and 300 reports with naturally occurring errors. Nine proprietary LLMs (under zero-shot and few-shot paradigms) and seven open-source LLMs (under fine-tuning) were evaluated, with performance compared against that of radiologists of varying seniority. Performance was measured by detection accuracy, Macro-F1 score, precision, recall, and mean absolute error across six categories. Fine-tuned open-source LLMs, notably Qwen3-14B, achieved a detection accuracy of 0.931 and a Macro-F1 of 0.739, approaching the performance of senior radiologists. Some fine-tuned open-source LLMs maintained performance despite smaller parameter sizes and outperformed most proprietary LLMs with vastly larger parameter counts. The fine-tuned Qwen3-14B demonstrated superior recognition capability for semantic errors such as redundancy, spelling, orientation, and unit or value errors. This study demonstrates that task-specific fine-tuning enables open-source LLMs to rival proprietary LLMs and expert radiologists in Chinese ultrasound report error detection, offering a locally deployable and privacy-compliant alternative for AI-assisted clinical quality control workflows. Question Can task-specific fine-tuning improve error detection by open-source LLMs in Chinese ultrasound reports and provide an effective approach to automated report quality control? Findings Task-specific fine-tuning enabled open-source LLMs to achieve Macro-F1 scores up to 0.739, approaching that of experienced radiologists (0.764) and outperforming most proprietary LLMs. Critical relevance statement Task-specific fine-tuning allows open-source LLMs to become feasible assistants in ultrasound report quality control workflows, offering a locally deployable, privacy-preserving, and regulation-compliant solution for enhancing reporting consistency and reducing diagnostic errors in high-volume ultrasound examination procedures. Question Can task-specific fine-tuning improve error detection by open-source LLMs in Chinese ultrasound reports and provide an effective approach to automated report quality control? Findings Task-specific fine-tuning enabled open-source LLMs to achieve Macro-F1 scores up to 0.739, approaching that of experienced radiologists (0.764) and outperforming most proprietary LLMs. Critical relevance statement Task-specific fine-tuning allows open-source LLMs to become feasible assistants in ultrasound report quality control workflows, offering a locally deployable, privacy-preserving, and regulation-compliant solution for enhancing reporting consistency and reducing diagnostic errors in high-volume ultrasound examination procedures.