Artificial Intelligence and Machine Learning in Neurology, 2 Volume Set

Abhishek Kumar, Pramod Singh Rathore, Sachin Ahuja, Manoj Manuja

• Publisher: Wiley-Scrivener
• Year: 2026
• Print ISBN: 9781394389100
• eBook ISBN: 9781394389117
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

Unlock the future of brain health with this indispensable guide, which offers a comprehensive exploration of how artificial intelligence and machine learning are revolutionizing the diagnosis, treatment, and management of complex neurological disorders.

As neurology grapples with some of the most challenging and pervasive health issues of our time, such as Alzheimers, Parkinsons, and stroke, AI offers the potential to transcend traditional barriers in treatment and management. Technologies such as machine learning models, neural networks, and cognitive computing are used to better understand and simulate brain functions, offering insights that are impossible for traditional analytical methods. Artificial Intelligence and Machine Learning in Neurology explores the pioneering intersection of neuroscience and artificial intelligence, offering a comprehensive examination of how machine learning and AI technologies are revolutionizing the fields of neurology and mental health. This book delves into cutting-edge research and practical applications of AI in diagnosing, treating, and managing neurological disorders. It discusses the development of intelligent diagnostic systems, personalized medicine approaches, and the potential of AI to analyze vast amounts of neurological data for insights. Additionally, the book addresses ethical considerations, challenges, and future prospects in the integration of AI into neurohealth sciences, making it an indispensable guide to this emerging technology.

Table of contents

• Cover
• Table of Contents
• Series Page
• Title Page
• Copyright Page
• Brief Contents of Volume 2
• Preface
• 1 Ethical Frameworks for AI-Driven Healthcare: Genetic and Epidemiological Perspectives on Ethical AI Frameworks
• 2 Ethical Challenges and Guidelines for AI Deployment in Healthcare: Urological and Gastroenterological Perspectives on Ethical AI Deployment
• 3 Bias Mitigation and Fairness in AI Healthcare Applications: Addressing Bias and Equity in AI-Driven Healthcare Solutions
• 4 Regulatory Compliance and Data Governance in AI-Driven Healthcare: Legal and Regulatory Considerations for AI-Driven Healthcare Solutions
• 5 Ensuring Responsible Data Use in Healthcare AI Applications: Radiological and Surgical Approaches to Responsible AI Data Usage
• 6 Implementing Secure Health Data Exchange with Blockchain: Orthopedic and Ophthalmological Insights into Secure Health Data Exchange
• 7 Securing Clinical Trial Data with Decentralized Technologies and Exploring Blockchain Applications in Modern Healthcare Management
• 8 Blockchain-Enabled Healthcare Ecosystems: Scalability, Security, and Interoperability
• 9 Advanced Threat Detection in Health Information Systems with Cybersecurity Technologies in Modern Healthcare Applications
• 10 Interoperability and Standardization in Healthcare System Integration: Interfacing Wearable Devices with Electronic Health Records
• 11 Fundamentals of Predictive Analytics in Healthcare: Nephrological and Pulmonological Fundamentals of Predictive Analytics
• 12 Advanced Techniques in Predictive Analytics: Ensemble Methods and Feature Engineering for Healthcare Predictions
• 13 Leveraging Machine Learning for Predictive Healthcare Models: Hematological and Rheumatologic Approaches to Machine Learning in Healthcare
• 14 AI-Driven Risk Assessment Models for Proactive Glaucoma Monitoring
• 15 Next-Generation AI/ML Algorithms for Health Monitoring: Deep Learning and Neural Network Architectures
• 16 AI-Driven Personalized Care for Chronic Disease Patients Tailoring Treatments and Interventions Using AI for Conditions Such as Diabetes, Hypertension, and Heart Disease
• 17 AI in Diabetes Management: Personalized Insulin Dosing and Glucose Monitoring Innovations in Diabetes Care Through AI for Continuous Glucose Monitoring and Insulin Therapy Optimization
• 18 Chronic Heart Disease Management with AI: Predictive Models and Early Interventions Using AI to Monitor Heart Disease Patients, Predict Adverse Events, and Recommend Preventive Measures
• Series Page
• Title Page
• Copyright Page
• Brief Contents of Volume 1
• 19 AI-Assisted Decision Support Systems in Chronic Disease Treatment: The Role of AI in Assisting Clinicians with Diagnosis, Treatment Recommendations, and Patient Management
• 20 Introduction to AI in Chronic Disease Management Overview of AI Technologies and Their Transformative Impact on Chronic Disease Care
• 21 Artificial Intelligence–Driven Identification of Biomarkers for Precision Medicine Advancements Through Bioinformatics in Healthcare Applications
• 22 Real-Time Chronic Disease Management with Smart Devices Integrating Internet-of-Things Technology in Healthcare Applications
• 23 Future Trends in Wearable Healthcare Technology: Innovations and Emerging Technologies in Wearable Devices
• 24 Challenges and Opportunities in Real-Time Data Processing: Advancements and Limitations in Real-Time Data Analytics
• 25 Overview of AI and Machine Learning Algorithms in Health Monitoring: Dermatological and Infectious Disease Applications of AI in Health Monitoring
• 26 Machine Learning Algorithms in Early Detection of Chronic Diseases Applications of Supervised and Unsupervised Learning for Early Diagnosis and Risk Prediction
• 27 Intelligent Neurohealth Systems: Revolutionizing Diagnosis and Therapy
• 28 Cognitive Computing and Neurobiology: A New Era in Brain Health
• 29 Natural Language Processing (NLP) in Chronic Disease Management Utilizing NLP to Extract Critical Information from Medical Records for Improving Chronic Disease Care
• 30 Remote Monitoring and Telehealth Solutions for Chronic Disease Care Integration of AI in Telemedicine for Continuous Patient Monitoring and Real-Time Interventions
• 31 Innovative Approaches in Mental Health Intervention Using Wearable Devices: Novel Therapeutic Modalities and Interventions
• 32 Artificial Intelligence–Driven Identification of Biomarkers for Precision Medicine Advancements Through Bioinformatics in Healthcare Applications
• 33 Future Directions and Opportunities in AI-Driven Healthcare: Family Medicine and Anesthesiology Future Directions in AI-Driven Healthcare
• 34 Future Directions in AI-Powered Medical Diagnostics: Innovations and Challenges in AI-Driven Diagnostic Technologies
• 35 AI in Cancer Screening and Early Detection
• 36 The Rhizobium-Legume Symbiosis and Biofortification in Sustainable Agriculture
• Index
• Also of Interest
• End User License Agreement