Machine Intelligence in Biomedical Data Modeling, Processing, and Analysis

Abstract

The primary focus of this paper review is to summarize the most important facts and findings regarding the use of Artificial Intelligence (AI) in the modeling, processing and analysis of biomedical data and to give an insight on the contributions of AI, Machine learning and Deep learning to the field of medicine. This study compiled and analyzed work published in the period between 1986 and 2021 related to the use of AI in medicine, its various applications and historical development, with a focus on papers published from 2015 until today, due to the accumulation and development of newer technologies. Out of a total of 117 papers reviewed, 52 were selected for a more detailed analysis and presented in a table summarizing the key points, advances, advantages and disadvantages of AI, its subfields and algorithms. The goal of this paper was to extract the most famous AI learning algorithms, past and current, and focus on the methods of modeling, processing and analysis by which these algorithms operate and perform tasks in order to help doctors and experts better understand the underlying mechanisms behind biological processes, and in some cases, even replace humans in data classification, identification, diagnosis and prediction of different conditions associated with diseases.

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