营养中的人工智能:基于案例的生成人工智能模型的比较
Ryan T Hurt1,2, Manpreet S Mundi2, Sara L Bonnes1
1Division of General Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA.
概括
基于大型语言模型 (LLM) 的人工智能助理 (AIA) 可以支持临床营养 (CN) 决策. 双子座显示出高度的相关性和清晰性,表明AIA可以帮助弥合医生营养护理方面的知识差距.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床营养学 临床营养学
背景情况:
- 由于慢性疾病和营养不良,临床营养 (CN) 越来越复杂.
- 在美国,CN的医生教育是有限的.
- 基于大型语言模型 (LLM) 的人工智能助理 (AIA) 是临床决策的新兴工具.
研究的目的:
- 评估四个基于LLM的AIA在复杂的临床营养病例中的表现.
- 评估CN专家对AIA反应的清晰度,相关性,证据和临床实用性.
主要方法:
- 四个基于LLM的AIA (ChatGPT,OpenEvidence,Gemini,Copilot) 的回顾性评估.
- 利用了五个复杂的临床营养病例.
- 答案是盲目的,并由五名CN医生专家使用八项评估工具进行审查.
主要成果:
- 所有的AIA都提供了临床上适当的反应.
- 双子座在相关性 (4.04) 和清晰度 (4.16) 方面获得了最高分.
- 总体满意度在3.08 (Copilot) 到3.84 (Gemini) 之间;引用质量和原创性是有限的.
结论:
- 基于LLM的AIA可以可靠地复制临床营养方面的专家推理.
- AIA显示出在支持缺乏CN专业专业知识的医生方面具有潜力,解决知识差距.
- 未来的应用可能包括AIA支持的营养教育电子咨询.
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