用生成性AI重新想象民族药理学:朝着包容性,道德和数据驱动的传统医学
Santanu Bhadra1, Charu Pundir1, Maria Mukherjee1
1Society for Ethnopharmacology, 23/3 Shaktigarh, Jadavpur, Kolkata 700032, India.
Pharmacological research
|October 21, 2025
概括
生成型人工智能 (GenAI) 可以将传统的民族药理学知识数字化,加速药物发现. 这项技术整合了多种不同的医疗系统,促进了全球传统医学的创新.
科学领域:
- 民族药理学 民族药理学
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 传统的医学知识系统为药物发现提供生物活性化合物,但面临着文档化和数字化挑战.
- 碎片化数据和与生物医学框架的有限整合阻碍了民族药理学的潜力.
- 生成型人工智能 (GenAI) 提出了数字化知识和加速研究的解决方案.
研究的目的:
- 批判性地检查GenAI在民族药理学中的应用.
- 突出GenAI在数字化传统知识,解码配方,预测相互作用和加速植物药物发现方面的作用.
- 为伦理的GenAI实施提出一个战略框架.
主要方法:
- 叙事综述综合了关于GenAI工具 (NLP,知识图,分子建模,多式联络数据集成) 的当前文献.
- 在亚洲,非洲和美洲土著医疗系统中分析现实世界的应用.
- 讨论利益相关者的利益,方法方面的创新和道德方面的考虑.
主要成果:
- 基因人工智能工具可以有效地将分散的传统知识数字化.
- 应用包括解码多草药配方和预测草药相互作用.
- 基因人工智能加速了新型植物药物的发现.
结论:
- 基因人工智能为民族药理学所面临的挑战提供了变革性的解决方案.
- 伦理的实施对于将传统知识与计算工具相结合至关重要.
- 基因人工智能 (GenAI) 在全球传统医疗系统中促进数据驱动的创新.
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