The promise of artificial intelligence (AI) has generated enthusiasm among patients, healthcare professionals, and technology developers who seek to leverage its potential to enhance the diagnosis and management of an increasing number of chronic and acute conditions. Point-of-care testing (POCT) increases access to care because it enables care outside of traditional medical settings. Collaboration among developers, clinicians, and end users is an effective best practice for solving clinical problems. A common set of clearly defined terms that are easily understood by research teams is a valuable tool that fosters these collaborations. We present brief, accurate, and clear descriptions of terms and techniques used to develop new device and decision support technologies in association with their most common applications to POCT. This lexicon of terms used to describe AI and machine learning techniques is quick reference for healthcare professionals, researchers, developers, and patients. Commonly used methods and techniques are tabulated and presented with text providing the context of their common usage and required data characteristics. Finally, we summarize model effectiveness measurement and the assessment of component features contributions. Artificial intelligence (AI) refers to non-human techniques that infer meaning from sets of data. It can produce generalizations, classifications, predictions, and can identify associations using automated learning methods. This guide provides an overview of these methods and their application to point-of-care testing.

中文翻译：

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通过应用机器学习技术和人工智能推进床旁测试。


人工智能 (AI) 的前景引起了患者、医疗保健专业人员和技术开发人员的热情，他们寻求利用人工智能的潜力来加强对越来越多的慢性和急性疾病的诊断和管理。护理点检测 (POCT) 增加了获得护理的机会，因为它可以在传统医疗环境之外提供护理。开发人员、临床医生和最终用户之间的协作是解决临床问题的有效最佳实践。研究团队易于理解的一组明确定义的通用术语是促进这些合作的宝贵工具。我们对用于开发新设备和决策支持技术的术语和技术及其最常见的 POCT 应用进行了简短、准确和清晰的描述。这个用于描述人工智能和机器学习技术的术语词典可供医疗保健专业人员、研究人员、开发人员和患者快速参考。常用的方法和技术以表格形式呈现，并以文本形式呈现，提供其常见用法和所需数据特征的背景。最后，我们总结了模型有效性测量和组件特征贡献的评估。人工智能 (AI) 是指从数据集中推断含义的非人类技术。它可以产生概括、分类、预测，并可以使用自动学习方法识别关联。本指南概述了这些方法及其在即时测试中的应用。