The term artificial intelligence broadly refers to the use of algorithms to discover connections and insights buried within data sets. Although these insights might have been there all along, they may have been difficult to notice. Machine learning is an adaptive process of interpreting information from real-world data sets by making use of computers that learn from experiences. These machines are fed data and learn from repetition, in much the same way as a child.
As artificial intelligence and machine learning develop, they’re likely to find a permanent home in medicine. In exploring concepts like predictive modeling, the medical use of algorithms, and deep learning, we can discover ways these methods could be applied to cardiology. Scientists working on this incredible technology hope that someday we may use machines to help deliver precision medicine.
With the advent of genome sequencing and other data-rich technologies, cardiologists are required to interpret huge amounts of information. Simultaneously, healthcare systems are constantly scrutinized for their efficiency with patients demanding more personalized care, faster. Because of this, physicians require advanced tools to keep up.
This is where machine learning can enhance patient care. Not only can computers crunch massive amounts of data for research, but they can also aid physicians in diagnosing and treating patients with greater precision.
In the future, health data will not be collected solely in medical offices. Wearable technology, already popular with consumers and practitioners alike, will help by taking health readings remotely. These readings can then be sent to doctors and fed through machines for interpretation. Amongst many possible applications, this type of technology will allow patients who may have previously required hospital stays to be monitored remotely. Additionally, patients once required to come in for regularly scheduled testing may be able to conduct a test from the comfort of their own home.
One benefit of using AI in cardiology is the amount of data that can be analyzed. In cardiac medicine, imaging modalities like CAT scans, MRIs, and intravascular ultrasounds create enormous amounts of data. These data sets could not easily be studied via traditional statistical methods.
Today, machine learning can help to automate cardiac imaging workflows by providing faster readings, interpretation, and diagnosis. This is only one of many sources of possible data sets in cardiology, meaning the possibilities for automation are vast.
Although there is some debate as to whether AI will replace doctors in the near future, many believe machines will simply improve medicine by making doctors more efficient.
Despite all the excitement surrounding the use of AI in medicine, machines are not perfect. Even the best algorithm is limited by the data it receives. It is the job of scientists to decide what data is required by these machines for them to be most effective in medical roles.
In the above video, Dr. Paul Friedman, chair of cardiovascular medicine at the male clinic, discusses emerging AI technology and how it can be useful in Cardiovascular medicine.