Machine Learning (ML) in Medicine: Review, Applications, and Challenges

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. Furth...

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Autores principales:	Amir Masoud Rahmani, Efat Yousefpoor, Mohammad Sadegh Yousefpoor, Zahid Mehmood, Amir Haider, Mehdi Hosseinzadeh, Rizwan Ali Naqvi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence (AI) machine learning (ML) diagnosis treatment medicine Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/92a6995fa5664d4bab582b6135616f0f
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spelling	oai:doaj.org-article:92a6995fa5664d4bab582b6135616f0f2021-11-25T18:17:40ZMachine Learning (ML) in Medicine: Review, Applications, and Challenges10.3390/math92229702227-7390https://doaj.org/article/92a6995fa5664d4bab582b6135616f0f2021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/22/2970https://doaj.org/toc/2227-7390Today, 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.Amir Masoud RahmaniEfat YousefpoorMohammad Sadegh YousefpoorZahid MehmoodAmir HaiderMehdi HosseinzadehRizwan Ali NaqviMDPI AGarticleartificial intelligence (AI)machine learning (ML)diagnosistreatmentmedicineMathematicsQA1-939ENMathematics, Vol 9, Iss 2970, p 2970 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence (AI) machine learning (ML) diagnosis treatment medicine Mathematics QA1-939
spellingShingle	artificial intelligence (AI) machine learning (ML) diagnosis treatment medicine Mathematics QA1-939 Amir Masoud Rahmani Efat Yousefpoor Mohammad Sadegh Yousefpoor Zahid Mehmood Amir Haider Mehdi Hosseinzadeh Rizwan Ali Naqvi Machine Learning (ML) in Medicine: Review, Applications, and Challenges
description	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.
format	article
author	Amir Masoud Rahmani Efat Yousefpoor Mohammad Sadegh Yousefpoor Zahid Mehmood Amir Haider Mehdi Hosseinzadeh Rizwan Ali Naqvi
author_facet	Amir Masoud Rahmani Efat Yousefpoor Mohammad Sadegh Yousefpoor Zahid Mehmood Amir Haider Mehdi Hosseinzadeh Rizwan Ali Naqvi
author_sort	Amir Masoud Rahmani
title	Machine Learning (ML) in Medicine: Review, Applications, and Challenges
title_short	Machine Learning (ML) in Medicine: Review, Applications, and Challenges
title_full	Machine Learning (ML) in Medicine: Review, Applications, and Challenges
title_fullStr	Machine Learning (ML) in Medicine: Review, Applications, and Challenges
title_full_unstemmed	Machine Learning (ML) in Medicine: Review, Applications, and Challenges
title_sort	machine learning (ml) in medicine: review, applications, and challenges
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/92a6995fa5664d4bab582b6135616f0f
work_keys_str_mv	AT amirmasoudrahmani machinelearningmlinmedicinereviewapplicationsandchallenges AT efatyousefpoor machinelearningmlinmedicinereviewapplicationsandchallenges AT mohammadsadeghyousefpoor machinelearningmlinmedicinereviewapplicationsandchallenges AT zahidmehmood machinelearningmlinmedicinereviewapplicationsandchallenges AT amirhaider machinelearningmlinmedicinereviewapplicationsandchallenges AT mehdihosseinzadeh machinelearningmlinmedicinereviewapplicationsandchallenges AT rizwanalinaqvi machinelearningmlinmedicinereviewapplicationsandchallenges
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Machine Learning (ML) in Medicine: Review, Applications, and Challenges

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