Many industries have been disrupted by the influx of new technologies in the Information Age. Healthcare is no different. Particularly in the case of automation, machine learning, and artificial intelligence (AI), doctors, hospitals, insurance companies, and industries with ties to healthcare have all been impacted—in many cases in more positive, substantial ways than other industries.
The healthcare market is now witnessing a new era wherein artificial intelligence is being applied to areas of research, imaging, diagnostics, and treatment. In cardiovascular medicine particularly, AI is being used in various ways from genomics to cardiac imaging analysis, yielding technology and tools that could potentially change diagnostic testing to improve patient care.
One of these promising methods is artificial neural networks (ANNs), a highly effective tool used in classification tasks, as well as to solve many important problems, such as signal enhancement, identification, and prediction of signals and factors. The important feature of ANNs is easy implementation. This enables them to be applied in cases when it is impossible to create a strict mathematical model but where there is a sufficiently representative set of samples. The other important characteristic of neural networks is their capacity to generalize input information and to give correct answers for “unfamiliar” data, which makes them effective in solving complicated classification problems. Today, ANNs are applied in clinical and genetic research. Attempts have been made to create diagnostic models for various diseases with the use of ANNs of different topologies. It is assumed that using complexes of signs of coronary heart diseases will allow ANNs to not only diagnose, but also to predict clinically significant events, myocardial infarction being the first.
As innovation pushes the capabilities of automation and digital workforces, more solutions such as ANN is foreseen to be making its mark in the healthcare domain in the near future.