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Artificial intelligence (AI) techniques such as machine learning, deep learning, and cognitive computing are promising and can potentially change the way medicine is practised. Over the past decade, several machine learning techniques have been used for cardiovascular disease diagnosis and prediction. An article published in Journal of the American College of Cardiology predicts that AI will result in a fundamental change in the area of cardiovascular (CV) medicine.
CV clinical care is currently exposed to various challenges such as high costs in patient care and increased readmission and mortality rates. Productive interactions between physicians and data scientists are needed to enable clinically meaningful automated and predictive data analysis.
AI has the potential to exploit big data to advance patient care. Deep learning AI using big data can be used in pattern recognition in heterogeneous syndromes and image recognition in CV imaging. For example, AI can classify new genotypes or phenotypes of heterogeneous syndromes, such as heart failure with preserved ejection fraction (HFpEF), Takotsubo cardiomyopathy, hypertrophic cardiomyopathy, hypertension, and coronary artery disease, leading to personalized targeted therapy.
Furthermore, big data has the potential to identify unknown risk factors in acute coronary syndrome (ACS), spontaneous coronary artery dissection (SCAD), or Brugada syndrome, and even the controversial usage of statins in the older population. AI techniques such as machine learning are not only revolutionizing the way physicians make various clinical decisions and diagnosis but also are improving the estimated CVD risk scores to automate prediction, the article says.
Cognitive computing involves self-learning systems using machine learning, pattern recognition, and natural language processing to mimic the operation of human thought processes. In cognitive computing, a system or a device is trained by machine learning or deep learning algorithms. IBM Watson, a well-known example of cognitive computing, continuously learns from datasets (e.g., EHR, social media) and predicts outcomes using multiple algorithms more accurately than humans. Cognitive computing can leverage machine learning to extend the ability of heart failure diagnosis efficiently by helping physicians discover numerous diagnosis patterns.
Cognitive technologies such as IBM Watson will likely be the standard in healthcare facilities, assisting physicians in decision making and predicting patient outcomes. AI will not replace physicians, but it is important that physicians know how to use AI to generate their hypotheses, perform big data analytics, and optimize AI applications in clinical practice to lay the foundation of a new era in precision CV medicine.
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