简化的伦理:在人工智能生成的放射学报告中平衡患者的自主性,理解和准确性
Hong-Seon Lee1, Seung-Hyun Song1, Chaeri Park1
1Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
BMC medical ethics
|October 15, 2025
概括
使用AI (LLMs) 简化放射学报告可能会导致准确性. 报告必须至少达到11年级的阅读水平,以保持临床准确性,尽管7年级可读性是理想的.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 临床沟通和患者理解
- 生物伦理学和人工智能实施
背景情况:
- 像GPT-4这样的大型语言模型 (LLM) 用于简化放射学报告,以更好地理解患者.
- 然而,过度简化可能会损害临床准确性,可能会破坏知情同意和患者自主权.
- 本研究探讨了人工智能生成的放射学报告中可读性和准确性之间的伦理平衡.
研究的目的:
- 确定人工智能生成的放射学报告保持临床准确性的最低阅读等级水平.
- 调查AI辅助医疗报告中不同可读性值的伦理含义.
- 在不牺牲准确性的情况下,确定LLM生成的放射学报告的最低可接受的读数水平.
主要方法:
- 追溯分析了500份CT和MRI报告.
- 将报告转化为17个版本 (1-17年级) 使用GPT-4 Turbo.
- 通过放射科医生评估和PubMed-BERTScore,以及可读性指标来评估临床准确性.
主要成果:
- 可读性得分与提示阅读水平有很强的相关性 (r=0.80-0.84).
- 临床准确性明显下降到11年级阅读水平以下.
- 在七年级水平上,20%的报告中存在可能改变患者管理的不准确性.
结论:
- 在提高报告可读性和保持临床准确性之间存在道德上的紧张关系.
- 当前的人工智能工具只能在11年级或更高的阅读水平上可靠地生成准确的报告.
- 伦理的人工智能实施需要多层次的沟通和模型透明度,以确保患者的自主性和理解.
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