artificial intelligence implication in cardiovascular medicine

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Artificial intelligence is a branch of information technology that imitates human intelligence, reasoning, understanding and memory. Artificial Intelligence is used in cardiovascular medicine to detect new genotypes and phenotypes of existing diseases, improve the quality of hospital care, decrease the cost of medicine, reduce the rate of re-admission and reduce the incidence of death of patients. For the past two years, artificial intelligence approaches have been used to improve the diagnosis, prevention and estimation of cardiovascular disease. This paper would explore the effects of Artificial Intelligence for cardiovascular medicine. In the next few years, Artificial Intelligence strategies like deep learning, cognitive computing, and machine learning will play a vital role in the evolution of cardiovascular medicine. Currently, cardiovascular medicine faces several challenges such as increased mortality rate, high readmission rates, rising cost of treatment and diagnosis, ineffective patient care and increased mortality rates among the patients. For meaningful automated and predictive data analysis, there must be a productive relationship between providers of health care services and data scientists. Currently, big data types such as cardiac imaging and human gut microbiome sequencing evolve, so fast and are difficult to analyze and store (Smarason, 2017). Artificial Intelligence can analyze big data and improve patient care. Cardiovascular diseases are caused by a complex combination of factors like genetic, environmental and behavioral elements. More technological advancements are therefore needed to conduct a more accurate and effective predictions of outcomes, instead of the traditional techniques that only assess risk factors. For instance, an Artificial Intelligence technique like deep learning technique conducts effective image recognition. It can also be utilized to conduct pattern recognition in heterogeneous symptoms. Other forms of Artificial Intelligence are utilized to classify new genotypes and phenotype of different symptoms like hypertension and heart failure. This leads to more customized and personalized treatment of cardiovascular conditions.

We have also experienced a significant evolution of biomedicine and medical informatics, thanks to Artificial Intelligence. The technology has provided different tools such as genetic algorithms and neural networks that doctors exploit in medicine. Also, the introduction of Smartphone and the increased utilization of medical based mobile applications enable easier monitoring of cardiovascular diseases by patients and providers. Artificial Intelligence technology is an effective tool in the diagnosis of heart diseases and heart failure. Techniques like multilayered perceptron neural network are used in diagnosis of diseases in the coronary artery. Computer Aided Diagnosis assists non-specialized medical staff in the analysis of heart failure.

Physicians and patients utilize several tools in the prediction of health. However, these tools have over time failed to match the complex nature of the human body. Cardiovascular arrests are in particular technical to predict. Nonetheless, there is hope. Scientists have demonstrated how technology can be used to increase prediction rates. Technology can also significantly reduce the rates of mortality. This paper cannot stress enough how critical it is to utilize Artificial Intelligence in prediction, diagnosis, and treatment of cardiovascular diseases. Providers also need to adopt artificial intelligence in patient care.

Millions of patients die annually from cardiovascular ailments like strokes, heart attacks, and malfunction of the circulatory system. In reaction to increased rates of mortality due to cardiovascular diseases, health care providers utilize guidelines proposed by the American Heart Association. These guidelines are founded on several risk factors of cardiovascular diseases such as cholesterol levels, blood pressure, physical inactivity and emotional stress. However, this approach is too simplistic and cannot be used to prevent the occurrence of cardiovascular conditions. There are a lot of complex interactions in the human body. Some of the fats in the biologic system are not risky to the human health. In fact, some protect the heart from various shocks. Artificial Intelligence, in essence, helps to explore the interactions in the human body. It analyzes massive amounts of data to predict cardiovascular conditions without any form of human intervention. Most of the data come from Electronic health records. Artificial Intelligence attempts to identify patterns in medical records that are consistent with cardiovascular conditions. It can identify some strange predictors of cardiovascular conditions. Some of these predictors are not included in the American Heart Association guidelines. They include risk factors like diabetes, mental disorders and increased intake of oral corticosteroids. However, Artificial Intelligence has one major limitation. As much as scientists can view information that is taken in and the solutions that result, they cannot understand what happens during the whole process. This makes it difficult for scientists to modify the algorithms to achieve the desired result. Even though doctors can utilize their expertise and knowledge on human anatomy and diseases, computers can make that process more efficient.

The main goal of Artificial Intelligence is to mimic the behavior and the functions of humans. Enhanced by increased availability of medical data and data analysis techniques, Artificial Intelligence has brought a paradigm shift in the healthcare industry, in particular, the prediction, diagnosis, and treatment of cardiovascular diseases (Smarason, 2017). The technology is applied on both structured and unstructured. The most common Artificial Intelligence techniques include deep learning, classical support vector machine, and natural language processing among several others. Artificial Intelligence is utilized in early diagnosis, treatment, and prediction of cardiovascular conditions like stroke and heart attacks.

Modern Artificial Intelligence technologies have sparked a lot of discussions in the healthcare industry. Their emergence has even fuelled discussions on whether they will replace human doctors sometime in the future. Despite the talk, Artificial Intelligence techniques would not replace human doctors; rather they will assists physicians make better clinical decisions and diagnosis. This technology will even assist doctors to make better judgments in critical areas of medicine. The application of Artificial Intelligence has been made possible by the development of data analytics techniques and increased availability of medical data (Krittanawong et al., 2017).

The benefits of Artificial Intelligence in the healthcare sector have massively been covered in the medical literature. It can apply complicated algorithms to extracts patterns from huge volumes of data. The acquired insights are used to better the clinical practice. The technology can also have self-learning and correcting capabilities that enhances accuracy depending on the outcome. AI assists doctors by providing the latest information from medical journals and periodicals. Moreover, the technology helps in reducing medical errors that are so common in the health care industry. It also extracts massive data from a population of data, which assist providers in making inferences on medical conditions (Hutson, 2017).

Before Artificial Intelligence systems are deployed in the health care industry, they must be taken through data that is generated from clinical practice like treatment, diagnosis, and screening. That way, they can understand the association different forms of information. Artificial Intelligence systems fall into two main classes (Fuster-Parra et al., 2016).The first major class is the machine learning strategy, which assists in the analysis of structured medical data like EP, imaging and genetic. Machine learning techniques help in clustering patients behaviors and predict the chance of diseases emerging. The second major class is the natural language processing. This technique assists in acquiring information form medical data that is unstructured like clinical notes that supplement structured information. The natural language processing technique helps to turn data that is in text form to readable and structured data. The machine learning techniques can after that analyzes the converted data.

Artificial Intelligence techniques have wide application in diagnosis, prediction, and treatment of stroke. More than 500 million people across the globe suffer from stroke. In fact, it is a major cause of mortality in China and the third leading cause of mortality in the United States. There is also the associated cost of treating and caring for patients, which puts a lot of pressure on the economy. In most cases, stroke is caused by blood clots in the arteries that block the flow of blood to the brain. Because there is lack of detection of early stroke signs, only a small percentage of patients receive early diagnosis and treatment. Machine learning techniques using algorithms enable an early detection of stroke before it becomes fatal. For diagnosis of stroke, physicians utilize techniques lie MRI, CT scan and neuroimaging. Machine learning assists in neuroimaging data, thus enhancing diagnosis. Machine learning techniques are also used to analyze CT scans extracted from patients. Moreover, the technology analyzes the performance of medication in stroke patients.

Automation in medicine using Artificial Intelligence will also make the health care industry more efficient. Examples of this automation include 3D printing and the use of medical robots. They will shift of the healthcare industry form treatment of diseases to prevention and early detection. The repetitive and monotonous activities will be conducted by robots freeing human doctors to perform productive and creative tasks. The benefits of Artificial Intelligence go beyond just automation of services in the health care industry. Artificial Intelligence can mine medical data and help physicians design personalized treatment for individual patients. According to research conducted by the University of Nottingham, Artificial Intelligence techniques can be used to predict risks for cardiovascular diseases by analyzing routine medical data. This technique is better than the current medical risks frameworks being utilized by doctors. Machine learning algorithms can analyze large volumes of clinical data, detect certain patterns that data and then use it to predict future events like the risk of stroke and heart disease. Artificial Intelligent techniques like neural network and logistic regression are more accurate in predicting cardiovascular diseases than the traditional medical methods. Artificial Intelligence techniques could significantly help in the fight against cardiovascular diseases which are the leading causes of death across the world. This can be done by increasing the identification of patients who are at an increased risk of contracting cardiac conditions and proposing remedial measures earlier (Tylman et al., 2016).

The emergence of Artificial Intelligence technology has captured the imagination of actors in the healthcare industry. It has made the prediction, diagnosis, and treatment of patient suffering from cardiovascular conditions more efficient than before. The technology has caused a paradigm shift in the way providers work by boosting the flow of work and patient care. The single biggest challenge facing clinicians in the modern healthcare industry is sifting through large volumes of clinical data. Artificial intelligence has made this easier and more patient. AI can analyze large volumes of medical data, detect patterns and help predict future risk events.


Fuster-Parra, P., Tauler, P., Bennasar-Veny, M., Ligęza, A., López-González, A. A., & Aguiló, A. (2016). Bayesian network modeling: a case study of an epidemiologic system analysis of cardiovascular risk. Computer methods and programs in biomedicine, 126, 128-142.

Hutson, M., (2017). Self-taught artificial intelligence beats doctors at predicting heart attacks. Science Magazine. Retrieved Sept. 24, 2017

Krittanawong, C., Zhang, H. J., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial Intelligence in Precision Cardiovascular Medicine. Journal of the American College of Cardiology, 69(21), 2657-2664. DOI: 10.1016/j.jacc.2017.03.571

Levine, B.A, (n.d). AI has brains—but it’s no MD. THE DIGITAL OB/GYN.

Smarason, H. (2017). Bringing Artificial Intelligence to Cardiovascular Medicine and Cancer: Genomics in Action. Haness Smarason. Retrieved Sept. 24, 2017, from

Tylman, W., Waszyrowski, T., Napieralski, A., Kamiński, M., Trafidło, T., Kulesza, Z., … & Wenerski, M. (2016). Real-time prediction of acute cardiovascular events using hardware-implemented Bayesian networks. Computers in biology and medicine, 69, 245-253

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