对大型语言模型在药物相互作用分析中的能力进行系统的映射审查
Himel Mondal1, Ipsita Dash2, Shaikat Mondal3
1Department of Physiology, All India Institute of Medical Sciences, Deoghar, India.
Expert review of clinical pharmacology
|September 26, 2025
概括
大型语言模型 (LLM) 在识别药物相互作用 (DDI) 中显示了可变的有效性. 需要专门的免费聊天机器人来改善临床实践中的药物安全性和DDI检测.
科学领域:
- 医疗保健中的人工智能
- 药物监督 药物监督 药物监督
- 临床决策支持系统 临床决策支持系统
背景情况:
- 药物相互作用 (DDI) 对全球健康构成重大风险,影响患者安全和治疗疗效.
- 大型语言模型 (LLM) 提供了可访问的工具,但它们在DDI分析中的实用性需要彻底调查.
- 本次审查批判性地评估了目前关于基于LLM的聊天机器人性能用于DDI识别的证据.
研究的目的:
- 评估公开可访问的LLM聊天机器人在识别药物相互作用中的有效性.
- 综合最近关于聊天机器人在DDI检测中的性能研究的发现.
- 通过人工智能识别差距和未来的研究方向,以改善药物安全.
主要方法:
- 根据PRISMA指南进行了系统审查.
- 在PubMed,Scopus和Web of Science进行了搜索,寻找2015年1月至2025年3月期间发表的研究.
- 有资格的研究重点是使用公开可访问的LLM聊天机器人来检测DDI.
主要成果:
- 九项研究 (2023-2025) 评估了聊天机器人,如ChatGPT,Bing AI和Google Bard,用于DDI识别.
- 性能不一致;ChatGPT (特别是GPT-4.0) 识别了更多的潜在DDI,但准确度不同.
- 在DDI检测方面,Bing AI和Google Bard的性能不那么可靠.
结论:
- 公共可访问的LLM聊天机器人为DDI检测提供了部分和可变的有效性.
- 开发专门的,免费可用的聊天机器人用于DDI识别是非常需要的.
- 未来的研究应该标准化评估方法并提高可访问性,以提高药物安全性.
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