In the past decade, there has been a tremendous increase in studies on the link between oral microbiome and systemic diseases. However, variations in study design and confounding variables across studies often lead to inconsistent observations. In this narrative review, we have discussed the potential influence of study design and confounding variables on the current sequencing-based oral microbiome-systemic disease link studies. The current limitations of oral microbiome-systemic link studies on type 2 diabetes mellitus, rheumatoid arthritis, pregnancy, atherosclerosis, and pancreatic cancer are discussed in this review, followed by our perspective on how artificial intelligence (AI), particularly machine learning and deep learning approaches, can be employed for predicting systemic disease and host metadata from the oral microbiome. The application of AI for predicting systemic disease as well as host metadata requires the establishment of a global database repository with microbiome sequences and annotated host metadata. However, this task requires collective efforts from researchers working in the field of oral microbiome to establish more comprehensive datasets with appropriate host metadata. Development of AI-based models by incorporating consistent host metadata will allow prediction of systemic diseases with higher accuracies, bringing considerable clinical benefits.

在过去的十年中，关于口腔微生物组与系统性疾病之间联系的研究有了巨大的增长。但是，研究设计的变化和研究之间的混杂变量通常会导致观察结果不一致。在这篇叙述性综述中，我们讨论了研究设计和混杂变量对当前基于测序的口服微生物系统疾病关联研究的潜在影响。本文对2型糖尿病，类风湿性关节炎，妊娠，动脉粥样硬化和胰腺癌的口腔微生物组-系统链接研究的当前局限性进行了讨论，随后我们就人工智能（AI）尤其是机器学习和深度学习的方式发表了看法这些方法可用于预测口腔微生物组的全身疾病和宿主元数据。AI在预测系统性疾病以及宿主元数据方面的应用要求建立具有微生物组序列和带注释的宿主元数据的全局数据库存储库。但是，此任务需要口腔微生物组领域的研究人员共同努力，以建立具有适当宿主元数据的更全面的数据集。通过整合一致的宿主元数据来开发基于AI的模型，将能够以更高的准确性预测系统性疾病，带来可观的临床益处。这项任务需要口腔微生物组领域的研究人员共同努力，以建立具有适当宿主元数据的更全面的数据集。通过整合一致的宿主元数据来开发基于AI的模型，将能够以更高的准确性预测系统性疾病，带来可观的临床益处。这项任务需要口腔微生物组领域的研究人员共同努力，以建立具有适当宿主元数据的更全面的数据集。通过整合一致的宿主元数据来开发基于AI的模型，将能够以更高的准确性预测系统性疾病，带来可观的临床益处。