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老年患者通过减药管理多重用药:人工智能工具作用的综述

Management of polypharmacy through deprescribing in older patients: a review of the role of AI tools.

作者信息

Al Meslamani Ahmad Z

机构信息

College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates.

出版信息

Expert Rev Clin Pharmacol. 2025 Jun;18(6):333-345. doi: 10.1080/17512433.2025.2519648. Epub 2025 Jun 13.

Abstract

INTRODUCTION

Deprescribing is crucial for improving patient safety since polypharmacy in older adults raises the likelihood of negative health outcomes. Artificial intelligence (AI) role in deprescribing has been rarely addressed.

AREAS COVERED

This review looks at how AI techniques are now affecting evidence-based deprescribing for older patients. Studies addressing AI applications, including chatbots, mobile apps, clinical decision support systems (CDSS), and machine learning (ML) algorithms, were found through a thorough literature search. Using a broad range of AI, deprescribing, and older adult-related keywords, relevant studies published up until November 2024 were found through thorough searches of electronic databases. This review finds that these technologies help physicians forecast adverse drug events, identify potentially inappropriate drugs, and enhance medication management.

EXPERT OPINION

AI-powered solutions have potential to improve patient outcomes and deprescribing procedures. However, issues including data quality, clinical acceptability, technology integration, and ethical considerations make practical adoption difficult. Extensive validation studies are required to confirm the safety and efficacy of these instruments. To make sure they enhance rather than complicate the deprescribing process, careful integration and ongoing assessment are necessary. Although AI can facilitate tailored deprescribing practice, it is essential to maintain human clinical touch and the patient-clinician interaction.

摘要

引言

由于老年人的多重用药增加了不良健康后果的可能性,因此减少用药对于提高患者安全性至关重要。人工智能(AI)在减少用药方面的作用很少被提及。

涵盖领域

本综述探讨了AI技术目前如何影响针对老年患者的循证性减少用药。通过全面的文献检索,找到了涉及AI应用的研究,包括聊天机器人、移动应用程序、临床决策支持系统(CDSS)和机器学习(ML)算法。通过广泛使用与AI、减少用药和老年人相关的关键词,对电子数据库进行全面搜索,找到了截至2024年11月发表的相关研究。本综述发现,这些技术有助于医生预测药物不良事件、识别潜在不适当的药物并加强药物管理。

专家意见

由AI驱动的解决方案有潜力改善患者结局和减少用药程序。然而,数据质量、临床可接受性、技术整合和伦理考量等问题使得实际应用变得困难。需要进行广泛的验证研究以确认这些工具的安全性和有效性。为确保它们能简化而非复杂化减少用药过程,需要进行仔细的整合和持续评估。虽然AI可以促进个性化的减少用药实践,但保持人文临床关怀以及患者与临床医生的互动至关重要。

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