Chapter 11 Data-driven AI for information retrieval of biomedical images
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Mert Akın İnsel
Abstract
The application of image processing tools to biomedical images has gained immense attention from researchers worldwide, as such technology can assist practitioners in various decision-making situations. Furthermore, with the increasing value of data in all sectors, including the biomedical healthcare industry, large datasets of biomedical images have become available to data scientists. As a result, successful data-driven artificial intelligence (AI) models have been developed, utilizing biomedical images as input and providing information about patients’ conditions as output, for various applications. These applications encompass diagnosis and disease detection, including cancer prognosis, monitoring, treatment, anatomy and physiology research, molecular imaging, drug development, and clinical trials, as well as biomedical research. As the knowledge base of AI algorithms continues to expand, the potential for groundbreaking advancements in biomedical imaging and its impact on healthcare will be significantly enhanced. In this regard, this chapter provides a review of the most recent literature, offering examples of AI applications in information retrieval from biomedical images. The algorithms used in each example are explicitly emphasized to facilitate the utilization of the most recent and best-performing AI algorithms. In conclusion, while machine learning (ML)-based AI models will remain valuable in most applications, deep learning (DL)-based AI models are expected to become even more prominent in the future, primarily due to the availability of large datasets of biomedical images. By presenting the importance of AI applications in obtaining information from biomedical images through the current literature, this section aims to shed light on researchers working in this dynamic field.
Abstract
The application of image processing tools to biomedical images has gained immense attention from researchers worldwide, as such technology can assist practitioners in various decision-making situations. Furthermore, with the increasing value of data in all sectors, including the biomedical healthcare industry, large datasets of biomedical images have become available to data scientists. As a result, successful data-driven artificial intelligence (AI) models have been developed, utilizing biomedical images as input and providing information about patients’ conditions as output, for various applications. These applications encompass diagnosis and disease detection, including cancer prognosis, monitoring, treatment, anatomy and physiology research, molecular imaging, drug development, and clinical trials, as well as biomedical research. As the knowledge base of AI algorithms continues to expand, the potential for groundbreaking advancements in biomedical imaging and its impact on healthcare will be significantly enhanced. In this regard, this chapter provides a review of the most recent literature, offering examples of AI applications in information retrieval from biomedical images. The algorithms used in each example are explicitly emphasized to facilitate the utilization of the most recent and best-performing AI algorithms. In conclusion, while machine learning (ML)-based AI models will remain valuable in most applications, deep learning (DL)-based AI models are expected to become even more prominent in the future, primarily due to the availability of large datasets of biomedical images. By presenting the importance of AI applications in obtaining information from biomedical images through the current literature, this section aims to shed light on researchers working in this dynamic field.
Chapters in this book
- Frontmatter I
- About the book V
- Preface VII
- Foreword IX
- Contents XI
- List of contributors XIII
- Chapter 1 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 1
- Chapter 2 Introduction to industry’s fourth revolution and its impacts on healthcare 33
- Chapter 3 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 67
- Chapter 4 E-health services and applications: A technological paradigm shift 101
- Chapter 5 Breaking down walls: The influence of virtual reality on accessible healthcare delivery 129
- Chapter 6 Digital twins and dietary health technologies: Applying the capability approach 165
- Chapter 7 Big Data analytics in healthcare system: A systematic review approach 185
- Chapter 8 Machine learning models for cost-effective healthcare delivery systems: A global perspective 199
- Chapter 9 Machine learning models for cost-effective healthcare delivery systems 245
- Chapter 10 Enhancing biomedical signal processing with machine learning: A comprehensive review 277
- Chapter 11 Data-driven AI for information retrieval of biomedical images 307
- Index 331
Chapters in this book
- Frontmatter I
- About the book V
- Preface VII
- Foreword IX
- Contents XI
- List of contributors XIII
- Chapter 1 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 1
- Chapter 2 Introduction to industry’s fourth revolution and its impacts on healthcare 33
- Chapter 3 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 67
- Chapter 4 E-health services and applications: A technological paradigm shift 101
- Chapter 5 Breaking down walls: The influence of virtual reality on accessible healthcare delivery 129
- Chapter 6 Digital twins and dietary health technologies: Applying the capability approach 165
- Chapter 7 Big Data analytics in healthcare system: A systematic review approach 185
- Chapter 8 Machine learning models for cost-effective healthcare delivery systems: A global perspective 199
- Chapter 9 Machine learning models for cost-effective healthcare delivery systems 245
- Chapter 10 Enhancing biomedical signal processing with machine learning: A comprehensive review 277
- Chapter 11 Data-driven AI for information retrieval of biomedical images 307
- Index 331
