Review Study on Machine Learning Impact on Clinical Practice

Abstract

In the healthcare sector, machine learning is quickly infiltrating many fields, from diagnostics to drug production and pharmacology, with tremendous opportunity to revolutionize the medical paradigm. Algorithms can be trained to identify medical imaging data abnormalities. Through use of machine learning relies on the collection and analysis of vast quantities of data, but descriptive analysis from huge data is challenged with the emergence of big data to recognize real patterns, while also restricting false categories and rendering definitive decisions on diagnostic and treatment choices. With the increase of antibiotic resistance, the use of machine learning techniques has been proving to be very successful in detecting new probiotic strains in a quicker and comparatively cheaper way. The aim of this analysis is to examine different aspects of medicine machine learning. It is important to remember that for several decades, obviously broad enough medical applications and appropriate learning algorithms have been available, and yet very few have substantially applied to clinical treatment, although there are hundreds of articles applying machine learning techniques to medical data. This lack of influence stands in stark contrast to the immense value to many other industries of machine learning. Therefore, part of my initiative would be to recognize the problems in the practice of medicine through approaches to machine learning and address applications that have never been addressed before.

Keywords-Artificial intelligence, Clinical practice, Healthcare, Machine learning, Medical science.