基于人工智能的新方法方法验证的挑战和机会
Thomas Hartung1,2, Nicole Kleinstreuer3
1Center for Alternatives to Animal Testing (CAAT), Doerenkamp-Zbinden-Chair for Evidence-based Toxicology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
ALTEX
|January 16, 2025
概括
人工智能 (AI) 在新方法方法 (NAM) 中的整合彻底改变了毒理学. 本综述详细介绍了人工智能驱动的验证策略,以加强化学品安全评估,旨在减少动物试验.
科学领域:
- 毒理学和计算科学 毒理学和计算科学
- 化学安全评估 化学安全评估
- 监管科学 监管科学
背景情况:
- 人工智能 (AI) 呈现了毒理学中的范式转变,提供了简化新方法方法 (NAM) 验证的潜力.
- 传统的验证方法面临着局限性,需要创新的化学安全评估方法.
- 人工智能融入NAM有望提高预测能力和高效的数据利用.
研究的目的:
- 探索在毒理学中验证基于AI的NAM的挑战,机会和未来方向.
- 为克服传统的验证局限性,提出人工智能驱动的框架,例如电子验证.
- 提出强有力的验证策略,并讨论AI在毒理学中的伦理考虑.
主要方法:
- 对NAM验证中人工智能的当前文献的综述.
- 用人工智能驱动的框架 (电子验证) 的概念化,以简化验证流程.
- 建议验证策略,包括分层方法,基准测试和不确定性量化.
主要成果:
- 基于AI的NAM提供了增强的预测毒理学和减少对动物试验的依赖.
- 关键的挑战包括数据质量,模型可解释性和监管接受性.
- 机遇在于高效的数据集成和改进的机制理解.
结论:
- 人工智能驱动的NAM在化学安全评估方面具有变革性的潜力.
- 强大的验证,道德考虑和协作努力对于成功的AI集成至关重要.
- 伴侣AI代理可以确保持续的方法有效性和适应不断发展的AI发展.
关键词:
人工智能 (AI) 是一种人工智能.生物医学研究是生物医学研究.化学品安全评估 化学品安全评估新方法方法 (NAMs) 是一种新方法方法.预测性毒理学 预测性毒理学监管机构接受的接受.验证验证的时间更多相关视频
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