2020
DOI: 10.1016/j.mayocp.2020.01.038
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Artificial Intelligence in Cardiology: Present and Future

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Cited by 170 publications
(110 citation statements)
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“…For instance, hospital systems are already using natural language processing technologies to extract diagnostic information from radiology and pathology reports and clinical notes. [19][20][21] As these capacities are shifted on to tasks for identifying clinically significant symptoms of SARS-CoV-2 infection, 22 hazards of embedding inequality will also increase. Where human biases are recorded in clinical notes, these discriminatory patterns will probably infiltrate the natural language processing supported AI models that draw on them.…”
Section: Key Messagesmentioning
confidence: 99%
“…For instance, hospital systems are already using natural language processing technologies to extract diagnostic information from radiology and pathology reports and clinical notes. [19][20][21] As these capacities are shifted on to tasks for identifying clinically significant symptoms of SARS-CoV-2 infection, 22 hazards of embedding inequality will also increase. Where human biases are recorded in clinical notes, these discriminatory patterns will probably infiltrate the natural language processing supported AI models that draw on them.…”
Section: Key Messagesmentioning
confidence: 99%
“…It is a nontechnical term that typically refers to ML-computer algorithms that are able to independently find patterns in large amounts of data. 1 Deep learning, a subfield of ML, uses neural networks to find these patterns and learn the relationships between provided input and desired outcomes. ML is already being employed in all cardiology subspecialties, with applications ranging from workflow optimization to disease diagnosis and prognostication, therapeutic intervention decision support, and research.…”
Section: Artificial Intelligence In Cardiologymentioning
confidence: 99%
“…ML is already being employed in all cardiology subspecialties, with applications ranging from workflow optimization to disease diagnosis and prognostication, therapeutic intervention decision support, and research. 1 Its uptake has been directed at 2 general goals: (1) providing human-like capabilities at scale and (2) developing new insights by analyzing existing data sets individually or in novel combinations (essentially moving beyond current human capacity).…”
Section: Artificial Intelligence In Cardiologymentioning
confidence: 99%
“…We used a feed‐forward neural network framework designed as a multi‐layer perceptron (MLP) 12,13 . The concept of a ‘perceptron’ originates from a probabilistic model for information storage and organization in the brain, which represents how information about the physical world is sensed, in what form is information remembered, and how information retained in memory to influence recognition and behaviour 14 .…”
Section: Methodsmentioning
confidence: 99%