Advancements and Future Directions in Machine Learning for Medical Diagnostics: A Comprehensive Review

Basant Sameh¹ , Nima Khodadadi², Ehsan khodadadi³, Marwa M. Eid⁴, S. K. Towfek⁵

¹Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology (DHIET), Mansoura 35111, Egypt

²Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA

³Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35111, Egypt

⁴ Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, AR 72701, USA

⁵Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA

Emails: CH1900072@dhiet.edu.eg; nima.khodadadi@miami.edu; Ehsank@uark.edu; mmm@ieee.org; sktowfek@jcsis.org

Abstract

Machine learning (ML) based techniques have enjoyed significant popularity in addressing the hostility of numerous problems in a range of applications, such as finance, marketing, production, environment, health care, and security. One of the most important distinctions between machine learning and human ways of thinking is their ability to observe patterns, make interpretations, reveal some hidden relationships, and analyze huge amounts of data. Machine learning (ML) technology can lead to improved specificity, sensitivity, predictability, and steadiness of such systems. Through this review, though, we will have an in-depth discourse on the application of machine learning in the field of medicine and how the latest technologies are mostly deployed in diagnostics. Medical applications that are widely used, including but not limited to machine learning solutions for medical chemistry, wearable sensors, cancer, the brain, and medical imaging, will be discussed in detail, with a focus on model adjustments to address the problems faced by the applications. In the course of the work, academics, practitioners, and decision-makers will have plenty of opportunities to utilize the findings, references, and insights of this study to improve their work and steer future research.

Keywords: Machine learning; Artificial Intelligence; Machine Learning Applications; Medical Field; ML in Healthcare.