Scholarlink

<p>The purpose of this study is to evaluate whether the reasoning model DeepSeek-R1 can function as a second-reader quality control (QC) tool for Chinese-language ultrasound reports. In this retrospective diagnostic-accuracy study with a parallel blinded review design, 500 deidentified finalized ultrasound reports were randomly sampled from 9711 eligible reports finalized in 2024 at a tertiary cancer center. DeepSeek-R1 and physician reviewer groups independently evaluated the same reports, and none of the review conditions had access to the outputs of the others or to the consensus reference standard. DeepSeek-R1 achieved 69.1% sensitivity and 98.1% specificity. DeepSeek-R1 showed numerically higher sensitivity than senior physicians (69.1% vs 47.1%), though this difference did not reach significance after Bonferroni correction (adjusted <i>p</i> = 0.147); specificity was identical at 98.1% for both. The model performed best for findings-impression discordance (36/42) and more modestly for completeness/template/indicator violations (7/21). In a post hoc exploratory OR-rule simulation, the combined workflow yielded 95.6% sensitivity (95% CI 87.8–98.5) and 96.3% specificity (95% CI 94.1–97.7). This retrospective single-center study provides workflow-level feasibility evidence that a reasoning model can serve as a high-specificity second-reader control for finalized ultrasound report text, with human review retained for local rules, exceptions, and final sign-off.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Reasoning Model-Assisted Second-Reader Quality Control of Chinese-Language Ultrasound Reports: A Retrospective Imaging Informatics Study

  • 期刊论文

  • 发表日期：

    2026年6月29日

查看全文
  • Zhenqi Zhang,
  • Zirui Jiang,
  • Yihan Qi,
  • Ziyue Hu,
  • Min Zhuang,
  • Juan Li,
  • Lu Wang

摘要

The purpose of this study is to evaluate whether the reasoning model DeepSeek-R1 can function as a second-reader quality control (QC) tool for Chinese-language ultrasound reports. In this retrospective diagnostic-accuracy study with a parallel blinded review design, 500 deidentified finalized ultrasound reports were randomly sampled from 9711 eligible reports finalized in 2024 at a tertiary cancer center. DeepSeek-R1 and physician reviewer groups independently evaluated the same reports, and none of the review conditions had access to the outputs of the others or to the consensus reference standard. DeepSeek-R1 achieved 69.1% sensitivity and 98.1% specificity. DeepSeek-R1 showed numerically higher sensitivity than senior physicians (69.1% vs 47.1%), though this difference did not reach significance after Bonferroni correction (adjusted p = 0.147); specificity was identical at 98.1% for both. The model performed best for findings-impression discordance (36/42) and more modestly for completeness/template/indicator violations (7/21). In a post hoc exploratory OR-rule simulation, the combined workflow yielded 95.6% sensitivity (95% CI 87.8–98.5) and 96.3% specificity (95% CI 94.1–97.7). This retrospective single-center study provides workflow-level feasibility evidence that a reasoning model can serve as a high-specificity second-reader control for finalized ultrasound report text, with human review retained for local rules, exceptions, and final sign-off.