Rudnisky Eli, Paudel Keshab
Biomedical Sciences, Burrell College of Osteopathic Medicine, Melbourne, USA.
Cureus. 2025 Jun 29;17(6):e86972. doi: 10.7759/cureus.86972. eCollection 2025 Jun.
Pharmacovigilance (PV) is a science that plays a crucial role in protecting patients by detecting adverse drug reactions (ADRs). PV can do this by collecting and analyzing data from a wide variety of healthcare sources. However, traditional PV methods face limitations, particularly in accurately and efficiently analyzing large datasets. This limitation leads to underreported ADRs, which negatively impact many patients. However, with the recent rise in artificial intelligence, PV as a science has the potential to improve. This can be done by incorporating different subsets of AI, such as machine learning (ML) and natural language processing (NLP), into PV. The aim of this study is to describe how integrating AI, specifically ML and NLP, into PV systems can improve data collection, data processing, and the detection of ADRs. A comprehensive literature search was conducted using PubMed and Google Scholar to examine studies that were conducted within the last 30 years. Twenty-eight studies were included in this paper. Inclusion criteria included articles that were written in English, articles focusing on PV as a science, ADRs, AI's current role in PV, and AI's potential role in PV. Exclusion criteria included studies that were not published in English and studies that were published more than 30 years ago. The findings from several systematic reviews that explore the implementation of AI into PV indicate that AI can improve PV by enhancing the efficiency and accuracy of detecting ADRs. Through ML algorithms, ADRs can be identified more quickly and accurately compared to traditional PV methods; while using the NLP model, AI is able to extract relevant patient data from unstructured data sources such as electronic health records (EHRs) and report certain drug interactions more accurately and efficiently. However, there are limitations to incorporating AI into PV. These include ethical, legal, and privacy concerns; interpretative limitations if certain datasets are incomplete and are missing information; the lack of current research; and the need to conduct more research on this topic to definitively determine whether AI should be incorporated into PV. With the exponential development of technology such as AI, there is a lot of promise in strengthening PV into a more accurate and efficient ADR detection system. While there is some research highlighting AI's potential to enhance PV, much more research needs to be conducted to fully substantiate this claim. Incorporating AI into PV does, however, have the potential to change ADR detection methods for the better.
药物警戒（PV）是一门通过检测药物不良反应（ADR）来保护患者的科学。PV可以通过收集和分析来自各种医疗保健来源的数据来实现这一点。然而，传统的PV方法面临局限性，特别是在准确有效地分析大型数据集方面。这种局限性导致ADR报告不足，对许多患者产生负面影响。然而，随着人工智能最近的兴起，作为一门科学的PV有改进的潜力。这可以通过将人工智能的不同子集，如机器学习（ML）和自然语言处理（NLP），纳入PV来实现。本研究的目的是描述将人工智能，特别是ML和NLP，集成到PV系统中如何能够改善数据收集、数据处理以及ADR的检测。使用PubMed和谷歌学术进行了全面的文献检索，以审查过去30年内进行的研究。本文纳入了28项研究。纳入标准包括用英语撰写的文章、专注于作为一门科学的PV的文章、ADR、人工智能在PV中的当前作用以及人工智能在PV中的潜在作用。排除标准包括非英语发表的研究以及30多年前发表的研究。几项探索将人工智能应用于PV的系统评价结果表明，人工智能可以通过提高检测ADR的效率和准确性来改善PV。通过ML算法，与传统的PV方法相比，可以更快、更准确地识别ADR；而使用NLP模型，人工智能能够从电子健康记录（EHR）等非结构化数据源中提取相关患者数据，并更准确、高效地报告某些药物相互作用。然而，将人工智能纳入PV也存在局限性。这些包括伦理、法律和隐私问题；如果某些数据集不完整且缺少信息，则存在解释局限性；目前研究的缺乏；以及需要对该主题进行更多研究，以最终确定是否应将人工智能纳入PV。随着人工智能等技术的指数级发展，将PV强化为一个更准确、高效的ADR检测系统有很大的前景。虽然有一些研究强调了人工智能增强PV的潜力，但需要进行更多研究来充分证实这一说法。然而，将人工智能纳入PV确实有可能更好地改变ADR检测方法。
