超越黑盒:使用可解释的因果人工智能在药物监测中分离信号和噪音
Renato Ferreira-da-Silva1,2, Ricardo Cruz-Correia3, Inês Ribeiro4,3
1Porto Pharmacovigilance Centre, Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-319, Porto, Portugal. rsilva@med.up.pt.
International journal of clinical pharmacy
|September 1, 2025
概括
人工智能 (AI) 增强药物监测 (PV) 以更快地检测安全信号. 然而,需要可解释和因果AI来克服黑盒限制并确保可靠的药物安全评估.
科学领域:
- 药物监督管理
- 人工智能
- 机器学习
背景情况:
- 机器学习 (ML) 模型在改善药物监测 (PV) 任务,如病例分类和信号检测方面表现有前途.
- 目前的机器学习模式往往充当"黑子",限制了对决策过程的透明度和信任.
- 传统的ML可能会延续自发报告系统中存在的偏差,例如报告不足和缺失数据.
研究的目的:
- 探索将人工智能整合到光伏中的潜力和挑战.
- 倡导在光伏领域向因果知情和可解释的人工智能模型过渡.
- 突出需要人工智能来增强,而不是取代药物安全方面的专家判断.
主要方法:
- 目前在光伏领域的人工智能应用.
- 讨论传统ML的局限性,包括缺乏可解释性和偏差放大.
- 探索可解释的AI (XAI) 和因果AI作为潜在的解决方案.
主要成果:
- 人工智能可以加速识别潜在的药物安全问题.
- 缺乏透明度和偏见的可能性是当前光伏人工智能的重大挑战.
- 可解释和因果AI方法提供更可解释和可靠的输出,但需要进一步开发和验证.
结论:
- 转向因果知情,可解释的人工智能模型对于可信和道德的人工智能至关重要.
- 关键优先事项包括整合因果推断,开发基准数据集,将输出与临床逻辑对齐,并建立严格的验证.
- 目标是通过透明可靠的人工智能工具增强专家判断力,以改善药物安全监测.
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