人工智能在毒理学领域的革命:临床精度,全球公平以及2030年路线图
1Universitat Rovira i Virgili, Laboratory of Toxicology and Environmental Health, School of Medicine, San Llorens 21, Reus, Catalonia 43201, Spain.
Toxicology letters
|February 28, 2026
概括
人工智能 (AI) 正在用更快,更准确的方法彻底改变毒理学. 诸如偏见和可解释性之类的挑战需要在毒理学中负责任地整合人工智能的路线图.
科学领域:
- 毒理学 毒理学 毒理学
- 人工智能的人工智能
- 计算毒理学计算毒理学
背景情况:
- 人工智能 (AI) 越来越多地应用于各种科学领域,包括毒理学.
- 人工智能为改善毒理学诊断,治疗,研究和教育提供了潜力.
- 目前的人工智能应用涵盖临床毒理学,预测建模,药物监测和培训.
研究的目的:
- 审查毒理学中人工智能应用的最新进展.
- 突出深度学习,强化学习和毒理学领域的生成人工智能等创新.
- 确定挑战,并提出一个路线图,在现场负责地采用人工智能.
主要方法:
- 文献综述综合了人工智能在毒理学方面的最新进展.
- 分析AI在临床毒理学,预测建模,药物监督和教育中的应用.
- 确定阻碍人工智能采用的主要局限性和挑战.
主要成果:
- 人工智能创新包括用于诊断的深度学习,用于剂量的强化学习和用于模拟的生成人工智能.
- 算法偏见,有限的解释性和验证挑战阻碍了临床和监管采用.
- 全球数字不平等对人工智能实施造成了进一步的限制.
结论:
- 在毒理学中负责任的AI整合需要解决偏见,可解释性和验证.
- 拟议的"ToxAI协议"为协调标准和基础设施提供了2030年的路线图.
- 嵌入公平性,可解释性和治理将使人工智能成为全球毒理学的基础基础设施.
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