Chumbita Mariana, Cillóniz Catia, Puerta-Alcalde Pedro, Moreno-García Estela, Sanjuan Gemma, Garcia-Pouton Nicole, Soriano Alex, Torres Antoni, Garcia-Vidal Carolina
Infectious Diseases Department, Hospital Clínic of Barcelona, 08036 Barcelona, Spain.
Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain.
J Clin Med. 2020 Jan 17;9(1):248. doi: 10.3390/jcm9010248.
The use of artificial intelligence (AI) to support clinical medical decisions is a rather promising concept. There are two important factors that have driven these advances: the availability of data from electronic health records (EHR) and progress made in computational performance. These two concepts are interrelated with respect to complex mathematical functions such as machine learning (ML) or neural networks (NN). Indeed, some published articles have already demonstrated the potential of these approaches in medicine. When considering the diagnosis and management of pneumonia, the use of AI and chest X-ray (CXR) images primarily have been indicative of early diagnosis, prompt antimicrobial therapy, and ultimately, better prognosis. Coupled with this is the growing research involving empirical therapy and mortality prediction, too. Maximizing the power of NN, the majority of studies have reported high accuracy rates in their predictions. As AI can handle large amounts of data and execute mathematical functions such as machine learning and neural networks, AI can be revolutionary in supporting the clinical decision-making processes. In this review, we describe and discuss the most relevant studies of AI in pneumonia.
利用人工智能（AI）辅助临床医疗决策是一个颇具前景的概念。推动这些进展有两个重要因素：电子健康记录（EHR）数据的可得性以及计算性能方面取得的进步。这两个概念在诸如机器学习（ML）或神经网络（NN）等复杂数学函数方面相互关联。的确，一些已发表的文章已经证明了这些方法在医学中的潜力。在考虑肺炎的诊断和管理时，AI与胸部X线（CXR）图像的使用主要用于早期诊断、及时的抗菌治疗，并最终实现更好的预后。与此相关的还有越来越多涉及经验性治疗和死亡率预测的研究。大多数研究通过最大化NN的能力，在预测中报告了较高的准确率。由于AI能够处理大量数据并执行诸如机器学习和神经网络等数学函数，因此AI在支持临床决策过程中可能具有革命性意义。在本综述中，我们描述并讨论了AI在肺炎方面最相关的研究。
