Ledziński Łukasz, Grześk Grzegorz
Department of Cardiology and Clinical Pharmacology, Faculty of Health Sciences, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Ujejskiego 75, 85-168 Bydgoszcz, Poland.
J Cardiovasc Dev Dis. 2023 May 6;10(5):202. doi: 10.3390/jcdd10050202.
As the world produces exabytes of data, there is a growing need to find new methods that are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant potential to impact the healthcare industry, which is already on the road to change with the digital transformation of vast quantities of information. The implementation of AI has already achieved success in the domains of molecular chemistry and drug discoveries. The reduction in costs and in the time needed for experiments to predict the pharmacological activities of new molecules is a milestone in science. These successful applications of AI algorithms provide hope for a revolution in healthcare systems. A significant part of artificial intelligence is machine learning (ML), of which there are three main types-supervised learning, unsupervised learning, and reinforcement learning. In this review, the full scope of the AI workflow is presented, with explanations of the most-often-used ML algorithms and descriptions of performance metrics for both regression and classification. A brief introduction to explainable artificial intelligence (XAI) is provided, with examples of technologies that have developed for XAI. We review important AI implementations in cardiology for supervised, unsupervised, and reinforcement learning and natural language processing, emphasizing the used algorithm. Finally, we discuss the need to establish legal, ethical, and methodical requirements for the deployment of AI models in medicine.
随着全球产生的数据量达到艾字节级别，人们越来越需要找到更适合处理复杂数据集的新方法。人工智能（AI）在影响医疗行业方面具有巨大潜力，而随着大量信息的数字化转型，医疗行业已然踏上变革之路。人工智能的应用已在分子化学和药物发现领域取得成功。在预测新分子药理活性的实验中，成本的降低以及所需时间的减少是科学领域的一个里程碑。人工智能算法的这些成功应用为医疗系统的变革带来了希望。人工智能的一个重要组成部分是机器学习（ML），它主要有三种类型——监督学习、无监督学习和强化学习。在这篇综述中，我们展示了人工智能工作流程的全貌，解释了最常用的机器学习算法，并描述了回归和分类的性能指标。我们还简要介绍了可解释人工智能（XAI），并列举了为其开发的技术示例。我们回顾了人工智能在心脏病学中用于监督学习、无监督学习、强化学习和自然语言处理的重要应用，并着重介绍了所使用的算法。最后，我们讨论了在医学中部署人工智能模型时建立法律、伦理和方法学要求的必要性。
