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用人工智能翻译眼科医学术语:一项比较理解研究
Michael Balas1, Alexander J Kaplan2, Kaisra Esmail3
1Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON, Canada.
Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
|December 12, 2024
概括
聊天GPT-4.0显著改善了医疗保健专业人员对眼科笔记的理解,展示了大语言模型 (LLM) 在医学翻译中的潜力.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 眼科通讯 眼科通讯
背景情况:
- 眼科笔记中的技术术语阻碍了医疗保健专业人员的理解.
- 有效的专业间沟通对于患者的护理和安全至关重要.
- 大型语言模型 (LLM) 为简化复杂的医学术语提供了潜在的解决方案.
研究的目的:
- 评估ChatGPT-4.0在翻译眼科技术术语方面的有效性.
- 将ChatGPT-4.0的翻译性能与其他领先的LLMs进行比较.
- 评估LLM生成的翻译对理解和临床实用性的影响.
主要方法:
- 一项涉及15个临床眼科笔记的观察性横截面研究.
- 笔记使用ChatGPT-4.0,ChatGPT-4o,Claude 3 Sonnet和谷歌双子来翻译.
- 家庭医生评估了原始和翻译的笔记的理解性和实用性;可读性使用Flesch分数进行评估;眼科医生发现了翻译错误.
主要成果:
- 与原始注释 (3.7/5.0和3.8/5.0分别) 相比,LLM翻译显著提高了理解能力 (4.7/5.0) 和实用性 (4.6/5.0);p < 0.001).
- 聊天GPT-4.0是最喜欢的模型 (8/15个案例),聊天GPT-4o也表现出强的表现.
- 虽然所有模型都有错误,但ChatGPT-4.0和4o的不准确性较少;翻译的笔记显示语言复杂性略有增加.
结论:
- 聊天GPT-4.0有效提高眼科笔记的可理解性,改善专业间的沟通.
- 在医学翻译方面,LLM是有前途的,但需要仔细实施和不断改进.
- 需要进一步的研究来验证跨不同医学专业和语言的LLM实用性.
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