Artificial Intelligence in Precision Cardiovascular Medicine.

By Chayakrit Krittanawong, Hongju Zhang, Zhen Wang, Mehmet Aydar, T. Kitai. Journal of the American College of Cardiology, 2017

939 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Informatics, Health Information Management

View Paper: https://www.semanticscholar.org/paper/4c0258d8996d53cb89b0c6a9571e1ef9fcbff4b5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Stephen J. H. Yang
2. Namkug Kim
3. J. Halperin
4. Zhen Wang
5. Damini Dey
6. Piotr J. Slomka
7. Mehmet Aydar
8. Chayakrit Krittanawong

In Health Informatics

1. Damini Dey
2. Piotr J. Slomka

In Health Information Management

1. Bin Li
2. Jonathan Guo

Citations in the corpus

• Artificial Intelligence Transforms the Future of Health Care. (2019, neutral): “14”
• Human-centered artificial intelligence in education: Seeing the invisible through the visible (2021, background): “…when we make big data inferences, we know what we do not know (known of unknown) and can subsequently employ reasoning for insight; it is more dif fi cult to reveal what we do not know (unknown of unknown), and AI can help identify many hidden values and unknown results (Krittanawong et al., 2017).”
• The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries (2018, background): “We would like to say that AI technology is the only solution.(3) IBM Watson is one of the leading AI healthcare support systems that can assist doctors in making efficient decisions.”
• Deep Learning for Prediction of Obstructive Disease from Fast Myocardial Perfusion SPECT: A Multicenter Study (2018, background): “in precision cardiology (15) or as an unsupervised tool for echocardiography imaging (8,16,17).”
• Deep into the Brain: Artificial Intelligence in Stroke Imaging (2017, methodology): “org data and multidisciplinary approaches used in making clinical decisions.(1,6) In particular, brain imaging, which is the key factor in stroke management and forms the basis for numerous complex go/no-go decisions, is an attractive subject for AI techniques.”
• Deep learning for cardiovascular medicine: a practical primer. (2019, background): “Deep learning is a specific type of ML inspired by the way that the human brain processes data, and enabled by hardware advances such as graphics processing units (GPUs), 16,18,19 vast cata-logues of labelled data, and advances in computer science theory.”