Miller D Douglas
From the Department of Medicine, Radiology and Population Health Sciences, Medical College of Georgia, Augusta, GA.
Cardiol Rev. 2020 Mar/Apr;28(2):53-64. doi: 10.1097/CRD.0000000000000294.
The computer science technology trend called artificial intelligence (AI) is not new. Both machine learning and deep learning AI applications have recently begun to impact cardiovascular medicine. Scientists working in the AI domain have long recognized the importance of data quality and provenance to AI algorithm efficiency and accuracy. A diverse array of cardiovascular raw data sources of variable quality-electronic medical records, radiological picture archiving and communication systems, laboratory results, omics, etc.-are available to train AI algorithms for predictive modeling of clinical outcomes (in-hospital mortality, acute coronary syndrome risk stratification, etc.), accelerated image interpretation (edge detection, tissue characterization, etc.) and enhanced phenotyping of heterogeneous conditions (heart failure with preserved ejection fraction, hypertension, etc.). A number of software as medical device narrow AI products for cardiac arrhythmia characterization and advanced image deconvolution are now Food and Drug Administration approved, and many others are in the pipeline. Present and future health professionals using AI-infused analytics and wearable devices have 3 critical roles to play in their informed development and ethical application in practice: (1) medical domain experts providing clinical context to computer and data scientists, (2) data stewards assuring the quality, relevance and provenance of data inputs, and (3) real-time and post-hoc interpreters of AI black box solutions and recommendations to patients. The next wave of so-called contextual adaption AI technologies will more closely approximate human decision-making, potentially augmenting cardiologists' real-time performance in emergency rooms, catheterization laboratories, imaging suites, and clinics. However, before such higher order AI technologies are adopted in the clinical setting and by healthcare systems, regulatory agencies, and industry must jointly develop robust AI standards of practice and transparent technology insertion rule sets.
被称为人工智能（AI）的计算机科学技术趋势并不新鲜。机器学习和深度学习人工智能应用最近都已开始对心血管医学产生影响。从事人工智能领域研究的科学家们早就认识到数据质量和来源对人工智能算法效率和准确性的重要性。有各种各样质量参差不齐的心血管原始数据来源——电子病历、放射影像存档与通信系统、实验室检查结果、组学等——可用于训练人工智能算法，以对临床结局（院内死亡率、急性冠脉综合征风险分层等）进行预测建模、加速图像解读（边缘检测、组织特征分析等）以及增强对异质性疾病（射血分数保留的心力衰竭、高血压等）的表型分析。现在，一些作为医疗设备的狭义人工智能产品，用于心律失常特征分析和先进图像反卷积，已获得美国食品药品监督管理局的批准，还有许多其他产品也在研发中。当下及未来使用融入人工智能的分析方法和可穿戴设备的医疗专业人员，在其明智的开发和实际的道德应用方面可发挥三个关键作用：（1）医学领域专家为计算机科学家和数据科学家提供临床背景；（2）数据管理员确保数据输入的质量、相关性和来源；（3）人工智能黑箱解决方案的实时和事后解释者，并向患者提供建议。下一波所谓的情境自适应人工智能技术将更接近人类决策，有可能增强心脏病专家在急诊室、导管室、影像科室和诊所的实时表现。然而，在临床环境以及医疗保健系统采用此类高阶人工智能技术之前，监管机构、行业必须共同制定强有力的人工智能实践标准和透明的技术引入规则集。
