Abstract

FOREFRONT OF ARTIFICAL INTELLIGENCE IN RADIOLOGY

Ajith S., Ezhilarasi M., Vigneshwaran L. V.* and Senthil Kumar M.

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Abstract

The purpose of this study is to explore the foundations for AI use in radiology, evaluate the immediate ethical and professional implications in radiology, and consider probable future evolution. In picturepopularity challenges, artificial intelligence (AI) systems, particularly deep learning, have demonstrated tremendous progress. Methods ranging from convolutional neural networks to variational auto encoders have discovered a plethora of applications in the field of clinical image analysis, moving it forward at a rapid pace. In the past, competent clinicians visually analysed clinical images for the detection, characterization, and tracking of diseases in radiology practise. AI algorithms excel in detecting complex patterns in imaging data and performing quantitative rather than qualitative tests of radiographic properties. In this Opinion piece, we lay up a well-known understanding of AI methods, particularly those pertaining to picture-based totally tasks. We look at how those techniques should affect a few facets of radiology, with a particular focus on patient safety.

Keywords: Medical imaging, Machine learning, Picture Archiving and Communication System, Data science.