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- W2968272384 abstract "Healthcare, conceivably more than any other area of human endeavour, has the greatest potential to be affected by artificial intelligence (AI). This potential has been shown by several reports that demonstrate equal or superhuman performance in medical tasks that aim to improve efficiency, diagnosis and prognosis. This review focuses on the state of the art of AI applications in cardiovascular imaging. It provides an overview of the current applications and studies performed, including the potential value, implications, limitations and future directions of AI in cardiovascular imaging. It is envisioned that AI will dramatically change the way doctors practise medicine. In the short term, it will assist physicians with easy tasks, such as automating measurements, making predictions based on big data, and putting clinical findings into an evidence-based context. In the long term, AI will not only assist doctors, it has the potential to significantly improve access to health and well-being data for patients and their caretakers. This empowers patients. From a physician’s perspective, reliable AI assistance will be available to support clinical decision-making. Although cardiovascular studies implementing AI are increasing in number, the applications have only just started to penetrate contemporary clinical care." @default.
- W2968272384 created "2019-08-22" @default.
- W2968272384 creator A5012303555 @default.
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- W2968272384 date "2019-08-09" @default.
- W2968272384 modified "2023-10-02" @default.
- W2968272384 title "Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist" @default.
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- W2968272384 cites W1708392634 @default.
- W2968272384 cites W1976196376 @default.
- W2968272384 cites W2031917779 @default.
- W2968272384 cites W2044861245 @default.
- W2968272384 cites W2080825898 @default.
- W2968272384 cites W2084783417 @default.
- W2968272384 cites W2086821304 @default.
- W2968272384 cites W2103004421 @default.
- W2968272384 cites W2103381850 @default.
- W2968272384 cites W2141690352 @default.
- W2968272384 cites W2162599450 @default.
- W2968272384 cites W2170646942 @default.
- W2968272384 cites W2254473384 @default.
- W2968272384 cites W2274227799 @default.
- W2968272384 cites W2308085519 @default.
- W2968272384 cites W2324798816 @default.
- W2968272384 cites W2330578068 @default.
- W2968272384 cites W2335916523 @default.
- W2968272384 cites W2345003174 @default.
- W2968272384 cites W2404618390 @default.
- W2968272384 cites W2408866005 @default.
- W2968272384 cites W2413889045 @default.
- W2968272384 cites W2427094903 @default.
- W2968272384 cites W2427373474 @default.
- W2968272384 cites W2536966079 @default.
- W2968272384 cites W2549857822 @default.
- W2968272384 cites W2579231017 @default.
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- W2968272384 cites W2747206966 @default.
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- W2968272384 cites W3023504947 @default.
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- W2968272384 doi "https://doi.org/10.1007/s12471-019-01311-1" @default.
- W2968272384 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6712136" @default.
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- W2968272384 hasPublicationYear "2019" @default.
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