从黑盒到空白盒:为下一代药物监督提供可解释的AI
Nirmala Suryadevara1, Vishnu Priya1, Sanjay Sharma1
1Shobhaben Pratapbhai Patel School of Pharmacy & Technology Management, SVKM's NMIMS (Deemed to Be University), Mumbai, India.
Expert opinion on drug safety
|February 6, 2026
概括
人工智能 (AI) 通过自动化数据分析来增强药物安全监测. 像SHAP和LIME这样的可解释AI (XAI) 方法对于监管信任和药物监管透明度至关重要.
科学领域:
- 药物监督和人工智能 药物监督和人工智能
- 监管科学 监管科学
- 可解释的人工智能 (XAI)
背景情况:
- 传统的药物监管面临着报告不足和数据问题等挑战.
- 人工智能为药物安全提供自动化和先进的模式识别.
- 可解释AI (XAI) 对于AI在药监监管中的监管接受至关重要.
研究的目的:
- 审查AI在药物监管方面的国际监管格局.
- 为建立监管信任提出XAI方法 (SHAP,LIME).
- 推在药监中使用人工智能的框架,以确保透明度和安全性.
主要方法:
- 对监管指南和学术数据库 (美国,欧盟,印度) 的系统文献搜索.
- 分析监管框架,包括欧盟人工智能法,FDA GMLP 和印度的负责任人工智能原则.
- 检查XAI技术的适用性在药物监测中的应用.
主要成果:
- 欧盟人工智能法案将光伏算法指定为高风险,需要人类监督和透明度.
- 美国和印度的监管原则也强调负责任的AI实施.
- SHAP和LIME被确定为实现可解释性和可审计性的关键方法.
结论:
- 建议在药物监管中使用AI,采用合适监管的XAI框架,整合人类监督.
- 该框架旨在加强安全信号检测,促进国际信任.
- 在可追溯性,治理和透明度方面的融合对于在药物安全方面采用人工智能至关重要.
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