Introduction to Artificial Intelligence

This book provides an introduction to Artificial Intelligence (AI) with a special focus on medical imaging. It covers topics such as programming languages, machine learning techniques, natural language processing, convolutional neural networks, and data preprocessing. It is a valuable resource for radiologists, computer scientists, and postgraduate students working on medical image analysis.

Format: Paperback / softback
Length: 165 pages
Publication date: 31 August 2023
Publisher: Springer International Publishing AG

This comprehensive book is designed to provide physicians and scientists with a solid foundation in Artificial Intelligence (AI), with a particular emphasis on medical imaging. The book's contents offer an introductory overview of the primary areas of AI currently applied in medical image analysis.

The journey begins with a foundational chapter that introduces essential terms used in AI, catering to novice readers. Subsequently, the book delves into a series of chapters, each dedicated to exploring a specific AI-related topic in depth.

In the second chapter, the focus shifts to programming languages and the automated tools available for developing AI applications in the field of medical imaging. This chapter highlights the essential tools and languages that facilitate the creation of AI systems capable of analyzing medical images.

The third chapter takes a deeper dive into the realm of traditional machine learning techniques, exploring algorithms such as random forests, support vector machines, and basic neural networks. It provides a comprehensive explanation of these algorithms and their applications in analyzing radiomics data, a crucial aspect of disease diagnosis.

In the fourth chapter, the applications of these machine learning techniques are extended to the analysis of radiomics data, enabling a deeper understanding of the algorithms used to build classifiers for the diagnosis of disease processes. This chapter highlights the significance of radiomics in precision medicine.

Chapter five delves into the realm of natural language processing, which has undergone a remarkable transformation in the analysis of complex radiological reports. This chapter explores the powerful tools and techniques that enable the extraction of valuable information from radiological reports, facilitating better patient care and disease management.

Chapter six provides a concise introduction to convolutional neural networks, which have revolutionized medical image analysis. This chapter highlights the automated image-based diagnosis, image enhancement (e.g., denoising), and protocoling capabilities that convolutional neural networks offer, transforming the field of radiology.

The penultimate chapter focuses on data preprocessing, a critical step in preparing data for use in various AI applications. It discusses the techniques and methodologies employed to clean, normalize, and transform data, ensuring its suitability for AI algorithms.

The book concludes with a chapter that showcases AI-based tools that are already in radiological practice, providing insights into their current applications and their potential future impact. This chapter underscores the growing importance of AI in the field of radiology and its potential to enhance patient care and outcomes.

Overall, this book serves as a valuable resource for radiologists, computer scientists, and healthcare professionals seeking to understand and leverage the power of AI in medical imaging. It provides a comprehensive and up-to-date exploration of AI techniques, algorithms, and applications, equipping readers with the knowledge and skills necessary to advance the field of radiology and healthcare.

Weight: 241g
Dimension: 203 x 127 (mm)
ISBN-13: 9783031259272
Edition number: 1st ed. 2023