Machine learning in medicine

Artificial intelligence and machine learning algorithms make it possible to enhance the predictive capabilities of modern medicine, as well as to create and improve new therapeutic approaches in the treatment of a wide variety of diseases. Machine learning in medicine is based on the analysis of large volumes of medical statistics, analysis and diagnostic data.

The processing of this information is carried out according to the specified algorithms. The use of such algorithms not only increases the efficiency of the work of medical personnel, relieving them of routine activities, but also improves the quality of forecasting. After all, machine learning algorithms can detect deviations that would otherwise go unnoticed. Thanks to this, machine learning plays a huge role in the early diagnosis of especially dangerous diseases.

One of the most promising areas is the analysis of the results of such examinations as ultrasound, magnetic resonance imaging, computed tomography, digital X-ray diagnostics, computed tomography. Basically, the implementation of machine learning algorithms in this area is carried out through convolutional neural networks, which are most suitable for working with visual graphical information. The attention mechanism also works well with such information, which is used to find relationships between different parts of the input and output data. The types of classification used are conventional, probabilistic or multiclass, depending on the problem.

Machine learning algorithms provide 90% early diagnosis accuracy using both training datasets and real medical data. This is especially important for the early prediction of cancer, as well as the development of treatment. For example, the algorithm locates the location and size of the tumor in tissue with high accuracy, and it also calculates the intensity, depth and direction of targeted radiation therapy to minimize side effects for healthy tissue.

For machine learning in medicine to be as effective as possible, convolutional neural networks require a huge amount of structured data. Obtaining real research data is often difficult due to lack of patient consent for their use. This problem can be circumvented by using generative adversarial networks, or GANs. It is a machine learning algorithm that is based on a combination of two neural networks. One of them is used to generate samples; in medicine, these are research results. Another network works as a filter, separating “real” results from “fake”, “with deviations”. As a result, a so-called “antagonistic game” arises between them, the results of which are opposite for both networks.

In wide use, Internet users GAN are familiar with the algorithms for creating images that are almost indistinguishable from real photographs. In medicine, the purpose of using the GAN is more serious: the generation of data from studies of healthy “patients” and data with deviations and pathologies. In this case, an important task is to classify by the type of pathology generated by the algorithm.

Given the relatively high workload on medical workers, machine learning algorithms also help to solve the problem of the impossibility of a personalized approach to different patients. This is a new model for the construction of medical care, including the selection of individual diagnostic and treatment tools that are optimally calculated for a particular patient according to the physiological, anatomical, and genetic parameters of his body.

Today, such algorithms are most widely used in oncology, as well as in pharmacology and genetics. In particular, the use of machine learning algorithms for predicting the likelihood of cancer based on the level of expression of certain genes in DNA looks very promising.

Machine learning is used to solve a variety of problems, from the development of new drugs to the calculation of the cost of insurance for a specific patient with his concomitant diseases – in the latter case, machine learning algorithms are also used in the financial sector. When developing new drugs, machine learning makes it possible to speed up and simplify many stages associated with predicting the effect of certain chemical compounds on certain systems and the body as a whole. This does not negate the clinical trial phase, but effectively precedes and complements it.

Also, new information technologies are becoming everyday assistants of doctors, making it possible to simplify access to an unstructured base of medical literature, making it easier to obtain valuable information from patients and build anamnesis. The direction of the use of machine learning in the field of mental health care is developing, in particular, artificial intelligence can predict the possible development of psychosis and other mental disorders based on the analysis of sound recordings.

AVADA MEDIA offers a solution to machine learning problems, including in the field of medicine. We develop artificial intelligence software products for customers around the world.