健康研究中生成人工智能聊天机器人的快速工程:传统,补充和整合医学研究人员的实用指南
Jeremy Y Ng1,2,3,4
1Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany.
Integrative medicine research
|January 7, 2026
概括
生成型人工智能 (GenAI) 聊天机器人为传统,补充和整合医学 (TCIM) 研究提供了宝贵的支持. 本指南提供了负责使用GenAI的策略,强调快速工程和批判性评估,以增强TCIM奖学金.
科学领域:
- 卫生研究 卫生研究 卫生研究
- 综合医学是一个整体的医学.
- 人工智能的人工智能
背景情况:
- 由大型语言模型 (LLM) 驱动的生成人工智能 (GenAI) 聊天机器人越来越多地被采用在健康研究中.
- 它们在传统,补充和整合医学 (TCIM) 中的应用尚未得到充分探索,尽管TCIM 面临复杂干预和文化细微差别等独特挑战.
研究的目的:
- 为TCIM研究人员提供有关负责的GenAI聊天机器人参与的实用,基于证据的指南.
- 概述快速工程的策略,以提高GenAI输出在TCIM研究中的相关性和准确性.
主要方法:
- 该指南详细介绍了将GenAI聊天机器人交互量身定制为TCIM方法论和认识论多样性的策略.
- 它介绍了整个研究生命周期的用例,从问题开发到实施规划,以示例和最佳实践.
主要成果:
- 基因人工智能聊天机器人可以支持各种研究活动,包括文献搜索,数据分析和写作.
- 重点是以人工智能生成的内容作为起点,需要由研究人员进行验证和审查.
结论:
- 负责任的GenAI聊天机器人使用,包括透明度和问责制,可以促进TCIM的创新.
- 在TCIM研究中,GenAI聊天机器人可以增强,但不能取代批判性思维,方法严谨或领域专业知识.
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