糖尿病及其并发症预测分析中的生成人工智能:叙事性审查
Ayokunle Osonuga1, David B Olawade2,3,4,5, Manisha Gore6
1Coltishall Medical Practice, NHS GP Surgery, Norfolk, UK.
Annals of translational medicine
|November 10, 2025
概括
生成型人工智能和大型语言模型 (LLM) 可以更早地检测糖尿病并发症,并个性化护理. 这些人工智能工具有望改善患者的治疗结果,并将糖尿病管理转向预防.
科学领域:
- 人工智能在医学中的应用
- 数字健康数字健康
- 精准医学是一门精准的医学.
背景情况:
- 2型糖尿病 (T2D) 是一个全球性的健康危机,具有严重的并发症,如视网膜病变和神经病变.
- 目前的诊断往往会发现T2D并发症迟到,阻碍早期干预.
- 生成型人工智能和LLM为改变糖尿病管理提供了新的可能性.
研究的目的:
- 审查用于糖尿病护理的生成AI和LLM的进展.
- 探索人工智能如何能够提前检测糖尿病并发症.
- 检查人工智能在开发糖尿病个性化干预措施中的作用.
主要方法:
- 一个叙事审查当前关于生成AI和糖尿病LLM的文献.
- 分析AI处理多维数据的能力 (成像,电子健康记录,遗传学,生活方式).
- 评估AI在预测分析和糖尿病并发症风险分层中的使用情况.
主要成果:
- 生成型人工智能在检测糖尿病视网膜病变和神经病变的早期风险因素方面表现有前途.
- 通过将非结构化数据 (笔记,患者结果) 与临床数据相结合,LLM提高了预测准确性.
- 人工智能工具在数据质量,透明度和隐私方面面临挑战,但提供主动监控.
结论:
- 生成型人工智能和LLM将通过早期检测和个性化治疗来彻底改变糖尿病管理.
- 将其整合到临床决策支持系统和精准医学中,可以减少全球糖尿病负担.
- 人工智能促进了糖尿病护理的转变,从反应性治疗转向积极预防.
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