使用人工智能模型为慢性病患者生成饮食建议:一个比较的横截面研究
Binyu Yang1, Wei Wei2, Caihong Liu2
1Department of Nephrology, Institute of Kidney Diseases, West China Hospital, Sichuan University, Chengdu 610041, China; West China School of Clinical Medicine, Sichuan University, Chengdu 610041, China.
Clinical nutrition (Edinburgh, Scotland)
|November 6, 2025
概括
人工智能 (AI) 模型为慢性病 (CKD) 提供了潜在的饮食指导. 然而,当前的AI输出需要在临床集成之前对准确性和安全性进行专家审查.
科学领域:
- 人工智能的人工智能
- 腎臟病學 (nephrology) 是一種醫學.
- 饮食科学 饮食科学
背景情况:
- 人工智能 (AI) 模型越来越多地用于慢性病 (CKD) 的饮食建议.
- 人工智能为慢性病患者提供饮食建议的准确性和安全性仍然存在担忧.
研究的目的:
- 评估三种AI模型 (ChatGPT,DeepSeek,Gemini) 在提供CKD饮食指导方面的表现.
- 为了评估AI模型对病:改善全球结果 (KDIGO) 2024指南的遵守.
主要方法:
- 对ChatGPT,DeepSeek和Gemini输出与KDIGO 2024指南进行比较分析.
- 专家和跨AI评估评估准确性,清晰性,完整性,实用性和安全性.
主要成果:
- 人工智能模型产生了结构化的饮食建议;DeepSeek在实用性方面得分最高.
- 在模型的准确性,清晰性,完整性或安全性得分方面没有发现统计学上显著的差异.
- 常见的局限性包括忽视禁忌,年龄特定需求和缺乏实时更新.
结论:
- 人工智能证明了CKD饮食支持的临床潜力.
- 专家验证,实时知识更新和强大的安全协议对于人工智能的可靠临床集成至关重要.
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