Asselbergs Folkert W, Fraser Alan G
Division Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands.
Institute of Health Informatics and Institute of Cardiovascular Science, University College London, 222 Euston Rd, London NW1 2DA, UK.
Eur Heart J Digit Health. 2021 Oct 18;2(4):721-726. doi: 10.1093/ehjdh/ztab090. eCollection 2021 Dec.
In 1955, when John McCarthy and his colleagues proposed their first study of artificial intelligence, they suggested that 'every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it'. Whether that might ever be possible would depend on how we define intelligence, but what is indisputable is that new methods are needed to analyse and interpret the copious information provided by digital medical images, genomic databases, and biobanks. Technological advances have enabled applications of artificial intelligence (AI) including machine learning (ML) to be implemented into clinical practice, and their related scientific literature is exploding. Advocates argue enthusiastically that AI will transform many aspects of clinical cardiovascular medicine, while sceptics stress the importance of caution and the need for more evidence. This report summarizes the main opposing arguments that were presented in a debate at the 2021 Congress of the European Society of Cardiology. Artificial intelligence is an advanced analytical technique that should be considered when conventional statistical methods are insufficient, but testing a hypothesis or solving a clinical problem-not finding another application for AI-remains the most important objective. Artificial intelligence and ML methods should be transparent and interpretable, if they are to be approved by regulators and trusted to provide support for clinical decisions. Physicians need to understand AI methods and collaborate with engineers. Few applications have yet been shown to have a positive impact on clinical outcomes, so investment in research is essential.
1955年，约翰·麦卡锡及其同事首次提出对人工智能进行研究时，他们指出，“原则上，学习的各个方面或智能的任何其他特征都可以被精确描述，从而使机器能够对其进行模拟”。这是否有可能实现，将取决于我们如何定义智能，但无可争议的是，需要新的方法来分析和解读数字医学图像、基因组数据库和生物样本库提供的大量信息。技术进步使得包括机器学习（ML）在内的人工智能（AI）应用得以在临床实践中实施，并且相关的科学文献正在激增。支持者们热情地认为，人工智能将改变临床心血管医学的许多方面，而怀疑论者则强调谨慎的重要性以及需要更多证据。本报告总结了在2021年欧洲心脏病学会大会的一场辩论中提出的主要对立观点。人工智能是一种先进的分析技术，当传统统计方法不足时应予以考虑，但检验假设或解决临床问题——而不是为人工智能寻找其他应用——仍然是最重要的目标。如果人工智能和机器学习方法要获得监管机构的批准并被信任为临床决策提供支持，就应该是透明且可解释的。医生需要了解人工智能方法并与工程师合作。目前很少有应用被证明对临床结果有积极影响，因此对研究的投资至关重要。
