Abstract
With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.
Highlights
Living in the big data era, with billions of terabytes of data generated every year, it might be challenging for humans to proceed with all the information
Will it change in the future? Will we let the AI perform actions itself, or will it remain only as a human tool? One thing is unquestionable—we must start accustoming ourselves to live alongside the machines that begin to equal or even surpass people in the processes of analyzing and deciding
There are of the learning, unsuperunsuperThere are three three principal principal learning learning models models of the machine learning (ML): ML: supervised supervised learning, vised learning, and reinforcement learning, which differ depending on the type vised learning, and reinforcement learning, which differ depending on the type of of data data input
Summary
Living in the big data era, with billions of terabytes of data generated every year, it might be challenging for humans to proceed with all the information. Machines are gaining an advantage over humans in typically human cognitive skills like analyzing and learning Their communication and understanding skills are improving quickly. The AI focuses on exploiting calculation techniques with advanced investigative and prognostic facilities to process all data types, which allows for decision-making and the mimicking of human intelligence. Such computational systems usually operate on large amounts of data and often integrate different types of input. The word “learning” stands here as the machine’s ability to become more effective with training experience Such a machine can quickly draw novel conclusions from the data that may be omitted by humans. Will it change in the future? Will we let the AI perform actions itself, or will it remain only as a human tool? One thing is unquestionable—we must start accustoming ourselves to live alongside the machines that begin to equal or even surpass people in the processes of analyzing and deciding
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