Abstract
Machine Learning (ML) and Artificial Intelligence (AI) methods are transforming many commercial and academic areas, including feature extraction, autonomous driving, computational linguistics, and voice recognition. These new technologies are now having a significant effect in radiography, forensics, and many other areas where the accessibility of automated systems may improve the precision and repeatability of essential job performance. In this systematic review, we begin by providing a short overview of the different methods that are currently being developed, with a particular emphasis on those utilized in biomedical studies.
Highlights
Artificial intelligence (AI), machine learning (ML) techniques, has experienced a significant rise in usage over the past decade for a variety of applications
While the terms Artificial Intelligence (AI) and Machine Learning (ML) are often used interchangeably in the general press, ML is just one branch of AI that deals with ways to provide a machine the capacity to "train," that is, to enhance efficiency in particular tasks depending on prior experience or data given [1]
In most cases, supervised learning entails reducing the value of an error function that explains the distance between both the computer forecasts and the classification algorithm in order to find the best way to translate the input to the output
Summary
Artificial intelligence (AI), machine learning (ML) techniques, has experienced a significant rise in usage over the past decade for a variety of applications. Whilst the use of such advanced devices in computed tomography is still in its early stages, specialists generally believe that ML is a genuinely disruptive technology that has the potential to profoundly change how imaging data is processed and used for therapeutic interventions and follow-up. The present rate of technological advancement is likely to produce additional advantages in the future [4]. With this descriptive review of the literature, we want to raise awareness of AI's present accomplishments and future clinical-related implications in the medical scientific establishment, as well as readers working in other disciplines who are unfamiliar with the technical elements of such technologies.
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