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评估大型语言模型在指导儿科折射误差治疗决策中的有效性
Daohuan Kang1, Hongkang Wu2,3, Lu Yuan1
1Department of Ophthalmology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
Ophthalmology and therapy
|February 22, 2025
概括
与其他AI模型相比,ChatGPT-4o在分析小儿近视管理的折射数据方面表现出卓越的准确性. 它提供了有前途的临床决策支持,尽管专家监督对于最佳的患者护理至关重要.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 儿科医学 儿科医学
背景情况:
- 儿科近视治疗需要基于复杂的折射数据做出精确的临床决定.
- 视敏度,轴长度和患者因素的变化挑战了最佳治疗的确定.
- 在分析儿科折射数据时评估AI性能对于推进临床支持至关重要.
研究的目的:
- 评估三种大型语言模型 (ChatGPT-3.5,ChatGPT-4o,Wenxin Yiyan) 在分析儿科折射数据中的性能.
- 将人工智能驱动的治疗建议的准确性和临床安全性与专家共识进行比较.
- 确定最有效的人工智能工具,以支持儿科近视管理中的临床决策.
主要方法:
- 使用ChatGPT-3.5,ChatGPT-4o和Wenxin Yiyan分析了100个儿科折射记录.
- 模型确定了干预需求和推的治疗方法 (眼镜,脊髓瘤学,低剂量氨酸).
- 使用全球质量评分 (GQS) 和临床安全评估,对眼科医生的共识进行了评估.
主要成果:
- 在确定干预需求方面,ChatGPT-4o获得了90%的准确性,明显优于温信益阳 (p<0.05).
- 聊天GPT-4o获得了最高的GQS (4.4±0.55),85%的回复被评为"好".
- 与ChatGPT-3.5 (12) 和Wenxin Yiyan (15) 相比,ChatGPT-4o在不完整/异常数据和更少的干预建议错误 (8) 的情况下表现优越.
结论:
- ChatGPT-4o在儿科折射数据分析中表现出更高的准确性和临床安全性.
- 这种人工智能模型显示出在儿科眼科中作为决策支持工具的潜力.
- 在使用人工智能用于近视管理中的临床决策时,需要持续的专家监督.
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