通过ChatGPT生成的放射学报告总结的质量和可理解性:调查研究
Alexis Sunshine1, Grace H Honce2, Andrew L Callen1
1Department of Radiology, University of Colorado Anschutz Medical Campus, 19th Ave. Mail Stop C278, Aurora, CO, 80045, United States, 1 303-724-3796, 1 303-724-3795.
JMIR formative research
|August 27, 2025
概括
大型语言模型 (LLM) 可以生成患者友好的放射学报告摘要,显著提高患者的理解和满意度. 虽然总体上是准确的,但总结需要监督,以确保所有关键的发现都得到适当的强调.
科学领域:
- 医学成像分析
- 医疗保健中的人工智能
- 患者沟通
背景情况:
- 放射学报告包含重要的医疗信息, 但患者往往难以理解.
- 患者的理解难度会导致焦虑和减少参与医疗保健决策.
- 大型语言模型 (LLM) 提供了创建简化,可访问和患者友好的摘要的潜力.
研究的目的:
- 评估ChatGPT生成的放射学报告摘要的准确性和清晰性.
- 与原始报告相比,评估患者对这些摘要的理解和满意度.
- 比较原始报告和人工智能生成的摘要的可读性.
主要方法:
- 使用ChatGPT总结了30份匿名放射学报告 (神经放射学).
- 放射科医生评估了总结的质量和准确性; 患者志愿者评估了可理解性和满意度.
- 使用经过验证的尺度和字数来测量可读性.
主要成果:
- 患者对理解 (98%),对术语的满意度 (91%) 和理解时间 (97%) 的信心显著提高.
- 放射科医生发现摘要高度代表原始报告 (83%),缺少或错误强调信息的情况很少.
- 没有发现伪造的结果;大多数患者 (83%) 认为摘要不会导致错误的结论.
结论:
- 通过ChatGPT生成的摘要可以提高患者的理解和满意度,同时保持高准确度.
- 轻微的不准确性 (遗漏,强调问题) 需要持续的验证和人类监督.
- 人工智能驱动的以患者为中心的摘要有望改善放射学方面的沟通.
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