研究与学术方法：使用人工智能

摘要(中文译文)

人工智能（AI）正在迅速改变医疗保健并拓展研究机遇，药学领域亦不例外。AI可为药学研究中的诸多挑战提供有效解决方案，例如循证处方方面持续存在的证据缺口、药物从发现到采用的漫长周期、复杂的数据环境，以及对更高效方法（包括定性方法）的需求。AI工具——从机器学习模型到大型语言模型（LLMs）——为数据分析、预测和内容生成提供了新的能力。药学研究人员可以利用这些技术来弥补用药管理方面持续存在的缺口，优化患者安全与结局，并简化许多研究工作流程。然而，成功整合需要理解AI能力的谱系、合适的应用场景以及潜在的局限性。AI必须根据研究问题、数据和机构资源进行量身定制。本文将指导药学研究人员识别其研究中AI的应用场景、选择和应用AI工具、降低风险、提供研究报告中使用AI的标准，并最大化其对科学发现和患者结局的益处。

Abstract

Artificial intelligence (AI) is quickly changing health care and expanding research opportunities, including in pharmacy. AI can offer effective solutions for numerous challenges in pharmacy research, such as persistent gaps in evidence-based prescribing, slow drug discovery-to-adoption timelines, complex data environments, and the need for more efficient methods, including qualitative methods. AI tools-ranging from machine learning models to large language models (LLMs)-offer new capabilities for data analysis, prediction, and content generation. Pharmacy researchers can harness these technologies to address persistent gaps in medication management, optimize patient safety and outcomes, and streamline many research workflows. However, successful integration requires understanding the spectrum of AI capabilities, appropriate use cases, and potential limitations. AI must be tailored to the research question, data, and institutional resources. This paper will guide pharmacy researchers in identifying use cases for AI in their research, selecting and applying AI tools, mitigating risks, providing standards for reporting the use of AI in research, and maximizing benefits for scientific discovery and patient outcomes.

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