Petersen Steffen E, Abdulkareem Musa, Leiner Tim
Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.
NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom.
Front Cardiovasc Med. 2019 Sep 10;6:133. doi: 10.3389/fcvm.2019.00133. eCollection 2019.
Artificial intelligence (AI) using machine learning techniques will change healthcare as we know it. While healthcare AI applications are currently trailing behind popular AI applications, such as personalized web-based advertising, the pace of research and deployment is picking up and about to become disruptive. Overcoming challenges such as patient and public support, transparency over the legal basis for healthcare data use, privacy preservation, technical challenges related to accessing large-scale data from healthcare systems not designed for Big Data analysis, and deployment of AI in routine clinical practice will be crucial. Cardiac imaging and imaging of other body parts is likely to be at the frontier for the development of applications as pattern recognition and machine learning are a significant strength of AI with practical links to image processing. Many opportunities in cardiac imaging exist where AI will impact patients, medical staff, hospitals, commissioners and thus, the entire healthcare system. This perspective article will outline our vision for AI in cardiac imaging with examples of potential applications, challenges and some lessons learnt in recent years.
利用机器学习技术的人工智能（AI）将改变我们所熟知的医疗保健领域。尽管目前医疗保健领域的人工智能应用落后于诸如基于网络的个性化广告等热门人工智能应用，但研究和部署的步伐正在加快，即将产生颠覆性影响。克服诸如患者和公众支持、医疗保健数据使用法律依据的透明度、隐私保护、与从并非为大数据分析而设计的医疗保健系统访问大规模数据相关的技术挑战，以及在日常临床实践中部署人工智能等挑战至关重要。心脏成像以及身体其他部位的成像很可能处于应用开发的前沿，因为模式识别和机器学习是人工智能的一项重要优势，与图像处理有着实际联系。心脏成像存在许多人工智能将对患者、医务人员、医院、医疗服务专员乃至整个医疗保健系统产生影响的机会。这篇观点文章将概述我们对心脏成像领域人工智能的愿景，并举例说明潜在应用、挑战以及近年来吸取的一些经验教训。
