Time and resources are two of the most important elements in healthcare systems around the world, and they are usually wasted. A correct and early diagnosis avoids numerous adverse consequences such as expenditure on further tests which may delay treatment plans and minimize survival rates. In many cases, there are false positives or even trials, to elucidate the situation. All these circumstances do not allow the research in an appropriate way or to take advantage of knowledge and expertise worldwide.

Machine learning as core in automated processes in the healthcare industry

Artificial intelligence, together with machine learning, is a field which is being introduced in the healthcare industry, since technology researchers are cooperating to develop the present situation. The algorithms used by computers allow us to gather an enormous volume of data in a quicker and more meticulous way than medical professionals do. The information can be exploited to uncover patterns and forecasting in order to improve diagnoses, planning treatments and ameliorate public health systems.

Since there has been an actual impact in the enhancement of the healthcare system, and as a result, in so many people’s lives and money saving, the introduction of AI and machine learning has proved to be very efficient. Therefore, there have been increased investments for AI and machine learning in the healthcare industry. Leader companies such as Microsoft, as well as many start-ups and minor organizations have embarked on the creation of their own AI healthcare projects.

Another advantage, already mentioned, is money saving. According to some statistics and reports, there is an estimation of approximately 100 billion pounds of money saving each year when clinical trials and research are improved thanks to the enhanced tools and insight that ease the choices of physicians, insurers and regulators.

The algorithms that machine learning uses work better when they have big amounts of data. In a field like the healthcare industry there is plenty of data that can be exploited to make the best use of it. Various processes such as storage systems, privacy and property concerns can help data sharing amongst people. There is also an enormous quantity of information that is not exposed to analysis and that could provide valuable data for all the players in healthcare organizations.

Artificial Intelligence and Diagnosis

The focus of AI work in the healthcare industry is mainly on disease identification and diagnosis. Sophia Genetics is one of the best examples, since they use AI to analyse DNA in order to diagnose different conditions. Other kind of resources are smartphone apps which draw conclusions and gather information about issues like blood pressure, haemoglobin levels, the function of some organs in people with chronic illnesses, or even assess the kind of coughs their owners suffer from. Health analysis and disease control is one of the main topics machine learning is developing nowadays.

One of the main reasons of human death worldwide is heart disease. Due to this fact, AI developers are focused on preventing and diagnosing this kind of health condition. The American College of Cardiology/American Heart Association’s (ACC/AHA) has published a list of some risk factors that can lead to heart disease; therefore, the process to determine a person’s risk factor can be carried out through paying attention to aspects such as blood pressure or age, amongst others. On the other hand, aspects regarding individual’s current medications or biological factors can also raise the chances of a heart disease or attack. These odds may be measured through machine learning, whose algorithms are able to measure and evaluate individual risk for a heart attack in a more efficient way than human beings are already doing.

A study from Stephen Weng at University of Nottingham in the United Kingdom is an example of the effort to work towards the improvement of algorithms in the machine learning field, since initial results obtained proved to be substantially better in heart attack predictions than the previously mentioned ACC/AHA list of risk factors.

Not only is AI able to detect heart diseases, but also other conditions such as cancer or mental illnesses. The aim of machine learning is to improve their processes to identify different abnormalities by reading CT scans and other imaging diagnostic tests in order to predict diseases. There are two prospects in the future: the end of radiology professionals or AI being their assistant in the health industry.