Ong Jasmine Chiat Ling, Chen Michael Hao, Ng Ning, Elangovan Kabilan, Tan Nichole Yue Ting, Jin Liyuan, Xie Qihuang, Ting Daniel Shu Wei, Rodriguez-Monguio Rosa, Bates David W, Liu Nan
Division of Pharmacy, Singapore General Hospital, Singapore, Singapore.
Department of Pharmacy, University of California, San Francisco, CA, USA.
NPJ Digit Med. 2025 Mar 28;8(1):182. doi: 10.1038/s41746-025-01565-7.
Medication-related harm has a significant impact on global healthcare costs and patient outcomes. Generative artificial intelligence (GenAI) and large language models (LLM) have emerged as a promising tool in mitigating risks of medication-related harm. This review evaluates the scope and effectiveness of GenAI and LLM in reducing medication-related harm. We screened 4 databases for literature published from 1st January 2012 to 15th October 2024. A total of 3988 articles were identified, and 30 met the criteria for inclusion into the final review. Generative AI and LLMs were applied in three key applications: drug-drug interaction identification and prediction, clinical decision support, and pharmacovigilance. While the performance and utility of these models varied, they generally showed promise in early identification, classification of adverse drug events, and supporting decision-making for medication management. However, no studies tested these models prospectively, suggesting a need for further investigation into integration and real-world application.
与药物相关的危害对全球医疗成本和患者预后有重大影响。生成式人工智能（GenAI）和大语言模型（LLM）已成为降低与药物相关危害风险的一种有前景的工具。本综述评估了GenAI和LLM在减少与药物相关危害方面的范围和有效性。我们筛选了4个数据库，以查找2012年1月1日至2024年10月15日发表的文献。共识别出3988篇文章，其中30篇符合纳入最终综述的标准。生成式人工智能和大语言模型应用于三个关键领域：药物相互作用识别与预测、临床决策支持和药物警戒。虽然这些模型的性能和效用各不相同，但它们总体上在药物不良事件的早期识别、分类以及支持药物管理决策方面显示出前景。然而，尚无研究对这些模型进行前瞻性测试，这表明需要进一步研究其整合及实际应用情况。
