使用大语言模型来帮助教学医学成像报告写作
Yingqian Chen1, Pei Xiang1, Qin Zhou1
1Department of Radiology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Medical teacher
|December 26, 2025
概括
免费的大型语言模型 (LLMs) 可以提高学生的医学成像报告质量. LLM反提高了准确性和标准化,但建议对教师进行监督以获得最佳结果.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 放射学报告 放射学报告
背景情况:
- 医学教育越来越多地利用人工智能工具.
- 放射学报告的标准化和准确性对于患者护理至关重要.
- 大型语言模型 (LLM) 提供了教育反的潜力.
研究的目的:
- 比较免费的大型语言模型 (LLMs) 的反效率.
- 评估LLM产生的反对学生成像报告质量的影响.
- 研究LLM反在提高报告准确性和标准化方面的作用.
主要方法:
- 评估了四个免费LLM的反质量.
- 评估学生的成像报告,使用住院培训尺度.
- 将接受LLM反的测试组与对照组进行了比较.
- 调查了学生对LLM反的满意度.
主要成果:
- 与Claude 3 OPUS相比,ChatGPT 3.5,ERNIE Bot v3.5和Tongyi v2.5提供了更好的反结构和逻辑.
- 所有LLM都表现出一定程度的幻觉.
- 与对照组相比,学生报告在测试组显著改善.
结论:
- 士反帮助学生批判性地评估和改进成像报告.
- 通过LLM生成的反可以提高报告能力.
- 在医学教育中使用LLM进行反时,教师监督是必不可少的.
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