Machine Learning in Medical Applications - A review of state-of-the-art methods

Aug 5, 2026

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Machine learning (ML) methods have been widely applied in recent years to address complex problems across multiple domains, with growing impact in medicine. ML systems leverage large datasets to uncover relationships, interpret evidence, and detect patterns that improve reliability, performance, predictability, and diagnostic accuracy. This survey reviews ML technologies used in medical diagnosis and discusses how models are adapted across key areas including cancer, medical chemistry, brain analysis, medical imaging, and wearable sensors. It offers references and direction for future research and development.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages38

Reading Time1 h 36 min

AccessFree to download

Introduction

Overview of Machine Learning

Types of Machine Learning

© This manuscript version is made available under the CC-BY-NC-ND 4.0 license [https://creativecommons.org/l](https://creativecommons.org/l)icenses/by-nc-nd/4 .0/ The definitive publisher version is available online at [https://doi.org/10.1016/j.compbiomed.2022.105458](https://doi.org/10.1016/j.compbiomed.2022.105458) Machine Learning in Medical Applications: A review of state-of-the-art methods Mohammad Shehaba , Laith Abualigahb,c,∗, Qusai Shambourd , Muhannad A. Abu-Hasheme , Mohd Khaled Yousef Shambourf , Ahmed Izzat Alsalibig , Amir H. Gandomih a Information Technology, The World Islamic Sciences and Education University. Amman, Jordan b Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan c School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, Malaysia. d Department of Software Engineering, Al-Ahliyya Amman University, Amman, Jordan e Department of Geomatics, Faculty of Architecture and Planning, King Abdulaziz University, Jeddah, Saudi Arabia fDepartment of Scientific Information and Services, Umm Al-Qura University, Mecca, Saudi Arabia g Information System College, Israa University, Gaza-Palestine h Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia. Abstract Applications of machine learning (ML) methods have been used extensively to solve various complex challenges in recent years in various application areas, such as medical, financial, environmental, marketing, security, and industrial applications. ML methods are characterized by their ability to examine many data and discover exciting relationships, provide interpretation, and identify patterns. ML can help enhance the reliability, performance, predictability, and accuracy of diagnostic systems for many diseases. This survey provides a comprehensive review of the use of ML in the medical field highlighting standard technologies and how they affect medical diagnosis. Five major medical applications are deeply discussed, focusing on adapting the ML models to solve the problems in cancer, medical chemistry, brain, medical imaging, and wearable sensors. Finally, this survey provides valuable references and guidance for researchers, practitioners, and decision-makers framing future research and development directions. Keywords: Machine learning, Medical field, Diagnosis, Healthcare, Medical applications 1. Introduction Machine learning (ML) refers to the tools, methods, and techniques employed in various fields (e.g. , medical) . It can help solve diagnostic problems in different medical domains [1], such as medical imaging, cancer diagnosis, and wearable sensor. ML is used to examine significant clinical parameters, such as extracting medical information and predicting diseases and development stages. Therefore, it helps in planning and supporting the patient’s status. Furthermore, it ensures efficient healthcare monitoring by helping in data analysis and sending intelligent warnings if necessary [2] . In specialized hospitals, the data of diagnoses for patients are considered medical records [3] . Executing a learning algorithm requires accurate encoding of patients’ information. Encoding is a simple step, but ML must start adequately; it should analyze information automatically and match it with similar problems solved previously. Thus, it helps the physician achieve an accurate, easy, and quick diagnosis of new cases. Nonspecialists and students can also use it to diagnose patients. ∗ Corresponding author Email addresses: [moh.shehab12@gmail.com](moh.shehab12@gmail.com) ( Mohammad Shehab), [Aligah.2020@gmail.com](Aligah.2020@gmail.com) (Laith Abualigah), [q.shambour@ammanu.edu.jo](q.shambour@ammanu.edu.jo) (Qusai Shambour), [mabohasm@kau.edu.sa](mabohasm@kau.edu.sa) (Muhannad A. Abu-Hashem), [myshambour@uqu.edu.sa](myshambour@uqu.edu.sa) (Mohd Khaled Yousef Shambour), [asalibi@israa.edu.ps](asalibi@israa.edu.ps) (Ahmed

FAQs

What does the survey focus on regarding machine learning in healthcare?

It reviews how machine learning is used in the medical field, emphasizing standard technologies and their influence on medical diagnosis.

Which major medical application areas are discussed in the review?

The review deeply discusses cancer, medical chemistry, brain applications, medical imaging, and wearable sensors, with attention to adapting ML models to each problem.

How does machine learning support diagnostic systems according to the abstract?

ML helps enhance reliability, performance, predictability, and diagnostic accuracy by examining data, discovering relationships, and identifying diagnostic patterns.

Aug 5, 2026

Upload by Eden