Artificial Intelligence in Radiology in the UAE

Abstract

Abstract Artificial intelligence (AI) has transformed the world and influences every aspect of our lives. AI has revolutionized industries, healthcare, and daily life tasks. Machine learning (ML) algorithms are used in AI to mimic how people think, reason, and learn. ML techniques can be classified into four categories based on the learning method and the desired outcome: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. A subfield of ML called deep learning (DL). A DL algorithm uses layers of neural networks to learn complex patterns from large amounts of data through advances in computing power and improved training techniques. In the healthcare sector, AI is an excellent tool for supporting the treatment of the growing population by decreasing the pressure on healthcare providers. In the medical field, diagnostic radiology is an area most in need of AI. Diagnostic imaging applications utilizing AI are promising, but they are constrained by a number of limitations. Data availability, for example, is one of the biggest challenges in programming AI algorithms. In the United Arab Emirates (UAE), AI in radiology has revolutionized medical imaging by enhancing diagnostic accuracy and streamlining workflows. It enables early disease detection and empowers radiologists to offer personalized treatment plans. However, it is worth noting that while AI adoption is on the rise, its full potential has not yet been fully realized. In this chapter, we will discuss types of machine learning algorithms, applications in healthcare focusing on diagnostic radiology, and their limitations.

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