毒理学的可能未来 - - 概率风险评估
Alexandra Maertens1, Eric Antignac2, Emilio Benfenati3
1Johns Hopkins University, Bloomberg School of Public Health and Whiting School of Engineering, Center for Alternatives to Animal Testing (CAAT), Doerenkamp-Zbinden Chair for Evidence-based Toxicology, Baltimore, MD, USA.
ALTEX
|January 12, 2024
概括
使用先进的人工智能进行概率风险评估,可以提供更精确的危险预测,但需要更多的专业知识和资源. 接受这种转变需要克服监管障碍和改善不确定性沟通.
科学领域:
- 毒理学 毒理学 毒理学
- 风险评估 风险评估
- 人工智能的人工智能
背景情况:
- 现有的风险评估方法存在局限性.
- 机器学习工具为危险和风险预测提供了新的方法.
- 人们越来越重视概率风险评估 (PRA).
研究的目的:
- 讨论基于人工智能的PRA的前景,挑战和未来实施.
- 探索从决定性风险评估过渡到概率风险评估.
- 解决人工智能驱动风险评估的复杂性和资源需求.
主要方法:
- 基于早期白皮书的讨论.
- 研讨会形式,讨论基于人工智能的概率性危险评估.
- 对暴露和危险数据的AI模型能力的分析.
主要成果:
- 人工智能模型可以预测特定的终点并估计风险评估的不确定性.
- 预计将转向概率和剂量依赖的危险结果.
- 在监管接受和所需的人类专业知识方面仍然存在挑战.
结论:
- 实施基于人工智能的PRA需要克服复杂性和资源需求.
- 未来的方向包括对数据较差的物质和开源软件使用内部值.
- 提高毒理学家的专业知识和透明的不确定性沟通至关重要.
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