临床肥胖通过情境意识的大型语言模型的镜头
Mohammad Kermansaravi1, Ricardo V Cohen2
1Department of Surgery, Minimally Invasive Surgery Research Center, Division of Minimally Invasive and Bariatric Surgery, Hazrat-E Fatemeh Hospital, Iran University of Medical Sciences, Tehran, Iran. mkermansaravi@yahoo.com.
Obesity surgery
|October 29, 2025
概括
大型语言模型可以通过专家证据来改进临床肥胖等医学定义. 将人工智能与人类监督相结合,可以增强临床定义,以获得更好的患者护理.
科学领域:
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
- 临床定义 提炼 提炼
背景情况:
- 复杂的医学定义需要不断改进.
- 目前对临床肥胖的定义存在局限性.
研究的目的:
- 评估大型语言模型 (LLM) 在完善临床肥胖定义.
- 为了评估ChatGPT-4,Gemini 1.5 Pro和Grok 3的功能.
主要方法:
- 使用结构化问题进行三阶段的定性研究.
- 模型提供了基线,上下文信息和生成定义.
- 结果经过专家审查,以确定准确性和临床相关性.
主要成果:
- 法律法规产生了没有上下文的概括定义.
- 背景指导 (兰塞特委员会报告) 提高了输出精度.
- 模型产生了精细的定义,解决了功能和心理社会方面的问题.
结论:
- 当LLM以证据为依据时,它有助于医学构造的知识合成.
- 人工智能与人类专业知识的整合可以创建适应性,基于证据的临床定义.
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