药物监管中的人工智能:朝着实时,可解释和全球药物安全系统的方向
Umair Ali1, Syeda Tayyaba Batool2, Raghabendra Kumar Mahato3
1Department of Pharmacy, University of Swabi, Swabi, Pakistan.
Annals of medicine and surgery (2012)
|February 12, 2026
概括
人工智能 (AI) 可以通过分析各种数据源来加强药物安全监测,以更快地检测药物不良反应. 挑战包括确保算法透明度,并解决对负责任的人工智能实施的监管监督问题.
科学领域:
- 药监和药物安全 药监和药物安全
- 医疗保健中的人工智能
- 计算药理学计算药理学
背景情况:
- 传统的药物监测系统正在与市场营销后药物安全数据的数量和复杂性日益增加而斗争.
- 传统方法的局限性阻碍了对药物不良反应 (ADRs) 的及时检测.
研究的目的:
- 探索人工智能 (AI) 在加强药物监督方面的应用.
- 讨论AI在识别来自集成数据源的安全信号方面的潜力.
- 突出AI在药物安全方面的挑战和未来方向.
主要方法:
- 利用人工智能,包括自然语言处理 (NLP) 和深度学习.
- 整合各种数据流:电子健康记录 (EHR),自发报告系统 (SRS) 和患者生成的数据.
- 分析数据,以便早期识别罕见和复杂的安全信号.
主要成果:
- 人工智能通过整合异质数据,为ADR检测提供可扩展的解决方案.
- 先进的NLP和深度学习有助于更早地识别安全信号.
- 人工智能集成可以显著减少药监报告延迟.
结论:
- 人工智能为改善营销后药物监测提供了有希望的进展.
- 解决算法透明度,偏见和监管监督对于负责任的AI采用至关重要.
- 未来的方向包括继续研究和开发强大的AI驱动的药监框架.
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