Abstract
In recent years, artificial intelligence (AI) has been progressively merging into the daily practice of many healthcare professionals. Radiology is a branch of medicine that can benefit from these new technological advancements, as it is a data-rich medical specialty and is well-placed to embrace AI. Specifically, radiologists are in a distinctive position to support the AI revolution because of their direct access to a significant amount of data. In turn, these AI tools can improve pathology detection by radiologists, thereby resulting in better, more accurate, and sooner diagnostics. The chapter aims to provide some new insights into AI concepts, tools, and their application in medical imaging. Several technologies are becoming more available in all imaging modalities, as the COVID-19 pandemic forced a rapid transition to a new era of digital health. In conclusion, the next generation of AI-based diagnostic imaging systems will surely have a serious impact on daily educational and healthcare institutions for the next generation.
TopMain Focus Of The Chapter
AI is described as the ability of computer systems to perform tasks commonly associated with intelligent beings. It often uses computer systems that analyze medical data to support the human specialist in the diagnostic process (e.g., Maaliw, Susa, et al., 2022). Radiologists are in a distinctive position to encourage the AI revolution in healthcare through their direct relationship with a remarkable amount of data. Thus, this chapter intends to give a review of some of the more important tools that can support teleradiology systems and some of the latest computer-aided detection tools available, and the results of their application in a real context, whether it is a profession or an educational level. Specifically, this chapter aims to achieve the following objectives:
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To understand the concepts of teleradiology and AI.
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Familiarize the different types of AI available in the market.
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Understand the types of educational and cognitive approaches in radiology education.
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Learn some of the AI tools and their impact in a real-life context.
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Identify the pros and cons of AI in educational and professional contexts.
Key Terms in this Chapter
Natural Language Processing: It refers to the application of computational techniques to the analysis and synthesis of natural language and speech.
Picture Archiving and Communication System: It is a storage system that allows depositing medical images in a nonphysical way thus available in various networks.
Principal Components Analysis: A statistical technique for reducing the dimensionality of a dataset.
Chronic Obstructive Pulmonary Disease: It refers to a group of diseases that cause airflow blockade.
Radiology: A medical specialty that diagnoses and treats diseases and injuries using imaging techniques, such as X-rays, computed tomography, magnetic resonance imaging, and ultrasound.
Convolutional Neural Networks: A type of artificial neural network specifically designed to process data that has a grid-like structure and is used in computer vision and image processing tasks, such as object recognition and classification.
Singular Value Decomposition: In linear algebra, it refers to the factorization of a matrix into three matrices.