Development of an artificial intelligence system for the forecasting of infectious diseases

A. A. Kuzin, R. I. Glushakov, S. A. Parfenov, K. V. Sapozhnikov, A. A. Lazarev
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Abstract

Aim . Here, we provided an overview of artificial intelligence (AI) approaches for developing a system for prediction of infectious diseases and designed a respective step-by-step protocol. Materials and Methods . Literature search in PubMed and Google Scholar and PubMed. Key Points . Infectious diseases impose a heavy burden on a healthcare, demanding the development of novel and efficient approaches to prevention as well as sensitive and specific diagnostic tests. Evolution of data science have led to the emergence of promising artificial intelligence (AI) algorithms and tools for the forecasting of infectious diseases. Employing machine learning algorithms, AI systems can rapidly analyze a large amount of data, extract specific disease patterns, and screen for the most efficient AI instruments in relation to specific tasks, thus contributing to prevention, diagnostics, and treatment of infectious diseases in the context of personalized medicine. Importantly, such AI-based systems can determine specific human motor patterns from videos and/or photographs in order to assist physicians in primary diagnosis. Integration of AI tools into the existing healthcare algorithms can be especially useful for public health.
传染病预测人工智能系统的开发
的目标。在这里,我们概述了用于开发传染病预测系统的人工智能(AI)方法,并设计了相应的分步协议。材料与方法。在PubMed, Google Scholar和PubMed中进行文献搜索。要点。传染病给医疗保健带来沉重负担,要求开发新颖有效的预防方法以及敏感和特定的诊断测试。数据科学的发展导致了用于预测传染病的有前途的人工智能(AI)算法和工具的出现。利用机器学习算法,人工智能系统可以快速分析大量数据,提取特定疾病模式,并筛选与特定任务相关的最有效的人工智能工具,从而为个性化医疗背景下的传染病预防、诊断和治疗做出贡献。重要的是,这种基于人工智能的系统可以从视频和/或照片中确定特定的人类运动模式,以帮助医生进行初步诊断。将人工智能工具集成到现有的医疗保健算法中,对公共卫生尤其有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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