AI in Disease Detection

Advancements and Applications

Rajesh Singh, Anita Gehlot, Navjot Rathour, Shaik Vaseem Akram

• Publisher: Wiley-IEEE Press
• Year: 2024
• Print ISBN: 9781394278664
• eBook ISBN: 9781394278688
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Vision & Pattern Recognition
• Index: Computer Science

Comprehensive resource encompassing recent developments, current use cases, and future opportunities for AI in disease detection

AI in Disease Detection discusses the integration of artificial intelligence to revolutionize disease detection approaches, with case studies of AI in disease detection as well as insight into the opportunities and challenges of AI in healthcare as a whole. The book explores a wide range of individual AI components such as computer vision, natural language processing, and machine learning as well as the development and implementation of AI systems for efficient practices in data collection, model training, and clinical validation.

This book assists readers in assessing big data in healthcare and determining the drawbacks and possibilities associated with the implementation of AI in disease detection; categorizing major applications of AI in disease detection such as cardiovascular disease detection, cancer diagnosis, neurodegenerative disease detection, and infectious disease control, as well as implementing distinct AI methods and algorithms with medical data including patient records and medical images, and understanding the ethical and social consequences of AI in disease detection such as confidentiality, bias, and accessibility to healthcare.

Sample topics explored in AI in Disease Detection include:

• Legal implication of AI in healthcare, with approaches to ensure privacy and security of patients and their data
• Identification of new biomarkers for disease detection, prediction of disease outcomes, and customized treatment plans depending on patient characteristics
• AI's role in disease surveillance and outbreak detection, with case studies of its current usage in real-world scenarios
• Clinical validation processes for AI disease detection models and how they can be validated for accuracy and effectiveness

Delivering excellent coverage of the subject, AI in Disease Detection is an essential up-to-date reference for students, healthcare professionals, academics, and practitioners seeking to understand the possible applications of AI in disease detection and stay on the cutting edge of the most recent breakthroughs in the field.

Table of contents

• Cover
• Series Page
• Title Page
• Copyright Page
• Contents
• About the Editors
• List of Contributors
• Preface
• Acknowledgments
• Chapter 1 Introduction to AI in Disease Detection — An Overview of the Use of AI in Detecting Diseases, Including the Benefits and Limitations of the Technology
• Chapter 2 Explanation of Machine Learning Algorithms Used in Disease Detection, Such as Decision Trees and Neural Networks
• Chapter 3 Natural Language Processing (NLP) in Disease Detection — A Discussion of How NLP Techniques Can Be Used to Analyze and Classify Medical Text Data for Disease Diagnosis
• Chapter 4 Computer Vision for Disease Detection — An Overview of How Computer Vision Techniques Can Be Used to Detect Diseases in Medical Images, Such as X-rays and MRIs
• Chapter 5 Deep Learning for Disease Detection — A Deep Dive into Deep Learning Techniques Such as Convolutional Neural Networks (CNNs) and Their Use in Disease Detection
• Chapter 6 Applications of AI in Cardiovascular Disease Detection — A Review of the Specific Ways in which AI Is Being Used to Detect and Diagnose Cardiovascular Diseases
• Chapter 7 Applications of AI in Cancer Detection — A Review of the Specific Ways in which AI Is Being Used to Detect and Diagnose Various Types of Cancer
• Chapter 8 Applications of AI in Neurological Disease Detection — A Review of Specific Ways in Which AI Is Being Used to Detect and Diagnose Neurological Disorders, Such as Alzheimer’s and Parkinson’s
• Chapter 9 AI Integration in Healthcare Systems — : A Review of the Problems and Potential Associated with Integrating AI in Healthcare for Disease Detection and Diagnosis
• Chapter 10 Clinical Validation of AI Disease Detection Models — An Overview of the Clinical Validation Process for AI Disease Detection Models, and How They Can Be Validated for Accuracy and Effectiveness
• Chapter 11 Integration of AI in Healthcare Systems — A Discussion of the Challenges and Opportunities of Integrating AI in Healthcare Systems for Disease Detection and Diagnosis
• Chapter 12 The Future of AI in Disease Detection — A Look at Emerging Trends and Future Directions in the Use of AI for Disease Detection and Diagnosis
• Chapter 13 Limitations and Challenges of AI in Disease Detection — An Examination of the Limitations and Challenges of AI in Disease Detection, Including the Need for Large Datasets and Potential Biases
• Chapter 14 AI-Assisted Diagnosis and Treatment Planning — A Discussion of How AI Can Assist Healthcare Professionals in Making More Accurate Diagnoses and Treatment Plans for Diseases
• Chapter 15 AI in Disease Surveillance — An Overview of How AI Can Be Used in Disease Surveillance and Outbreak Detection in Real-World Scenarios
• Index
• EULA