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Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person's behavior in solving problems or his ability for learning. Furthermore, ML is a subset of artificial intelligence. It extracts patterns from raw data automatically. The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare. In this paper, we first present a classification of machine learning-based schemes in healthcare. According to our proposed taxonomy, machine learning-based schemes in healthcare are categorized based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real environment) and applications (diagnosis, treatment). According to our proposed classification, we review some studies presented in machine learning applications for healthcare. We believe that this review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions. [ABSTRACT FROM AUTHOR]

Background: The implementation of artificial intelligence technology in health care improves disease prediction, classification, and diagnosis, benefiting both patients and healthcare providers. a surge in artificial intelligence popularity, owing largely to an enormous increase in computational capabilities and an even greater increase in data generation. The study's purpose was to conduct a bibliometric analysis on healthcare-related artificial intelligence research from the years 2000 to 2021. Methods: The Scopus dataset has been used to find and retrieve all existing and referenced healthcare-related artificial intelligence research published in English. Based on bibliometric indicators, the rate of publication growth, the subject area, and the top active countries, institutions, journals, and funding sponsors were analyzed. Results: The search identified non-duplicated 5,019 papers. During the years 2000 to 2009, there were fewer publications, but they increased in the subsequent years. Moreover, research released after 2012 constitutes 88.88% of the total publications. Overall, 96.85% of the included studies have been published in 9 countries. About 41.84% of the studies included were from the US. The technology keywords that appeared most were “Machine Learning”, “Electronic health records”, and “Natural language processing”. Furthermore, Covid-19, Diabetes, Mental Health, Asthma, Dementia, and Cancer are some of the disease-related keywords that appeared frequently in healthcare-related artificial intelligence research. Conclusions: The study carried out a thorough bibliometric study on healthcare-related artificial intelligence research, which will help researchers, legislators, and practitioners understand the field's growth and the prerequisites for responsible use of artificial intelligence technology within the healthcare system.