The availability of multidimensional data together with the development of modern techniques for data analysis represent an exceptional opportunity for clinical pharmacology. Data science-defined in this special issue as the novel approaches to the collection, aggregation, and analysis of data-can significantly contribute to characterize drug-response variability at the individual level, thus enabling clinical pharmacology to become a critical contributor to personalized healthcare through precision dosing. We propose a minireview of methodologies for achieving precision dosing with a focus on an artificial intelligence technique called reinforcement learning, which is currently used for individualizing dosing regimen in patients with life-threatening diseases. We highlight the interplay of such techniques with conventional pharmacokinetic/pharmacodynamic approaches and discuss applicability in drug research and early development.

中文翻译：

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模型通知的人工智能：精确剂量的强化学习。

多维数据的可用性以及现代数据分析技术的发展为临床药理学提供了难得的机会。在本期特刊中定义为数据收集，汇总和分析的新颖方法的数据科学可以显着地有助于表征个体水平上药物反应的变异性，从而使临床药理学能够通过以下方式成为个性化医疗保健的重要贡献者精确加药。我们提议对实现精确剂量的方法学进行简要回顾，重点是一种称为强化学习的人工智能技术，该技术目前用于个性化危及生命的患者的给药方案。