AI or Artificial Intelligence is mainly designed by researchers for doctors to predict heart disease. Basically, they have developed the specific model for the coronary artery disease utilizing Artificial Intelligence and the relevant set of almost 600 variables, which outperformed the model built utilizing 27 variables selected by medical experts. 

In fact, Artificial Intelligence also recognized certain non-obvious factors like “home visit from their GP”, as a good predictor of patient mortality. Although AI research is still in its early years, these early studies already set up how AI is set to transform cardiac care. It is specifically relevant in recent times as the cardiovascular diseases are still the number one killer in the world, resulting in 31% of all global deaths, and this is also the priciest condition to treat.

Here are 7 different ways in which Artificial Intelligence is revolutionizing the cardiovascular care:

1. AI aided diagnostics

One of the biggest impacts of Artificial Intelligence in cardiac care will be there in diagnosing cardiovascular diseases. Typical diagnostic pathways involve three stages. The very first stage is measuring the electrocardiogram (ECG) at rest. Anomalies in this stage results in a combination of semi-invasive tests like ECG stress test, stress echocardiography, and also chest CT scan. Anomalies in these tests lead the invasive angiography. Researchers and companies are already utilizing AI so that it can easily predict the anomalies quickly, cheaply and accurately without using the third invasive step.

2. AI aided cardiac imaging

AI is used in cardiovascular research as it enhances the live visualization of the heart in real-time from the low-resolution grayscale echocardiography images. In fact, a couple of years ago, this technology were not really accessible. AI enhances the live visualization only by the color-coding. Philips’ echocardiography uses an AI called HeartModelᴬ⋅ᴵ⋅ to actually build the 3D model of the patient’s heart from echocardiography images. HeartFlow provides the similar AI-powered FDA cleared software solution so that it can recognize coronary artery disease using chest CT scans but provides a more comprehensive 3D output for the cardiologists.

3. AI aided therapy selection

In recent times, Artificial Intelligence is used in medical research in a wide manner. And just like some other medicinal things, it is also used in the therapy selection as well. One of the hardest challenges for the cardiologists, hospital systems, patients and their families is only to decide the cost of care and risk suggested by the experts. KenSci reportedly uses reliable and useful machine learning to predict patient risks of acquiring diseases including heart disease. Babylon Health’s Healthcheck is considered as the chatbot that uses AI to give patients a quick assessment and understand their health. Corti labs perhaps take this a step further and utilize the AI to be familiar with out-of-hospital cardiac arrests and assist emergency dispatchers to make critical life-saving decisions.

4. AI aided continuous monitoring

Nowadays, smart technologies are always available and one can also use this for their health as well. Eventually, wearable technologies like Fitbit and Apple Watch that continuously monitor the consumer’s heart rate, activity and also location, serve as a very good platform for building AI tools, which can simply predict early warning signs of lifestyle diseases including cardiovascular anomalies. Cardiogram’s DeepHeart, which works with Apple Watches, is mainly considered as the semi-supervised AI learning for cardiovascular risk prediction. As consumer medical devices start utilizing more accurate single lead ECG sensors, like Apple Watch 4 and AliveCor’s Kardia Band the output from the AI will hopefully be even more reliable.

5. Treatment stage in diagnostics

When the diagnostics are done, there comes the treatment stage, and data science is a great tool for simplifying it. Using the data of people with an already confirmed diagnosis, this is quite possible to give every new patient with a personalized treatment and care. The experts have also said that using AI in the diagnostics, the methodology becomes extremely smooth and easy and it is also quite time-consuming.

6. Post-Treatment Care

Unfortunately, even the most effectual treatment sometimes can’t guarantee that the patient will not even face recurrence or suffer from pain and diverse complications after leaving the hospital. Along with the assistance of data science, medical experts can simply predict the potential changes and develop suitable post-treatment program. The data is collected from smart technology or you can say wearable devices of patients suffering from the same diseases. This type of strategy improves the usage of hospital resources. For example, this is very simpler for the doctors to understand if any of the patients have to stay in a clinic for a few days longer. In this way, they can decide if this person will need a bed and hospital drugs or no.

7. Computed tomography

Cardiac CT has made the leap forward in the last decade, emphasizing the visualization of stenosis right in the coronary tree, plaque characteristics, coronary calcification, and scoring and more recently the modeling of flow. Promising chances for AI in CT have actually automated noise reduction while retaining optimal imaging quality, and also the avoidance of all-encompassing coronary angiography (ICA) for the determination of significant stenosis.

In recent times, with the arrival of technology, Artificial Intelligence has gained massive popularity amongst the people. In fact, in recent times, Artificial Intelligence is actually on the verge of redefining how cardiovascular care has been delivered to patients. The researchers and companies have implemented AI in every step of the process, from continuous monitoring of the basal heart rate for early warning signs to a quick and efficient noninvasive diagnosis of cardiac conditions. 

Artificial intelligence is also making the later stages of care pathways quite efficient like the real-time visualization of the cardiac anomaly and subsequent therapy selection. Though, the question, which simply stays to be answered is will this advanced technology, in the long run, be able to bring down cost and time of cardiovascular care for patients or not.