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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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用大型语言模型来进行胃肠病学研究:一个概念框架.

Parul Berry¹, Rohan Raju Dhanakshirur², Sahil Khanna³

¹Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA.

Therapeutic advances in gastroenterology

|April 2, 2025

概括

大型语言模型 (LLM) 在胃肠病学中为改善临床决策和研究提供了显著的潜力. 提出了一个结构化的框架,以指导LLMs在医疗保健中的安全和有效整合,解决关键挑战.

关键词:

人工智能的人工智能是人工智能.一个基本的框架框架.生成型的人工智能 (GAI)医疗保健医疗保健医疗保健医疗保健

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科学领域:

人工智能在医学中的应用
胃肠病学的创新胃肠病学的创新
临床决策支持系统临床决策支持系统

背景情况:

大型语言模型 (LLM) 正在改变医疗保健,在临床决策,研究和患者管理方面提供帮助.
在胃肠病学中,LLM在临床决策支持,数据提取和患者教育方面表现有前途,但面临诸如偏见和监管障碍等挑战.

研究的目的:

提出一个结构化的框架,将LLMs纳入胃肠病学实践.
通过使用C型肝炎治疗来证明这个框架的现实应用.
确保准确性,安全性和临床相关性,同时减轻AI风险.

主要方法:

框架开发包括目标定义,团队组建,数据处理,模型选择,微调,校准和幻觉缓解.
检索增强生成和微调模型适应性的评估.
整合偏差检测,从人类反中强化学习和结构化提示工程来提高可靠性.
解决伦理和监管方面的考虑 (HIPAA,GDPR,DECIDE-AI,SPIRIT-AI,CONSORT-AI). 解决了这些问题.

主要成果:

该框架提供了一种全面的方法来整合胃肠病学LLM.
详细介绍了在临床环境中提高LLM准确性,安全性和可靠性的具体方法.
伦理和监管合规策略在整个框架中整合在一起.

结论:

在胃肠病学中,LLM具有显著的潜力,可以提高决策,研究效率和患者护理.
负责任的部署需要严格的偏见缓解,透明度和持续验证.
未来的研究应该优先考虑多机构验证和人工智能辅助的临床试验,以建立LLM作为可信的工具.