Artificial Intelligence and Machine Learning in Neurology, 2 Volume Set

• Autore: Kumar Abhishek
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2026; ©2026
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (846 pages)
• Nota di contenuto: Cover -- Volume One -- Series Page -- Title Page -- Copyright Page -- Contents -- Brief Contents of Volume 2 -- Preface -- Chapter 1 Ethical Frameworks for AI-Driven Healthcare: Genetic and Epidemiological Perspectives on Ethical AI Frameworks -- 1.1 Introduction -- 1.1.1 The Role of AI in Healthcare -- 1.1.2 Importance of Ethical Frameworks -- 1.2 Ethical Considerations in AI-Driven Healthcare -- 1.2.1 Medical Ethics -- 1.2.2 Challenges in Applying Ethical Principles to AI -- 1.3 Genetic Perspectives on Ethical AI Frameworks -- 1.3.1 Personalized Medicine and AI -- 1.3.2 Data Protection of Genetics Information -- 1.3.3 Informed Consent and Genetic Testing -- 1.3.4 Equity in Access to Genetic Treatments -- 1.4 Ethical Frameworks for AI in Genetics -- 1.4.1 Principles of Autonomy, Beneficence, and Justice -- 1.4.2 Ensuring Patient Control Over Genetic Data -- 1.4.3 Utilizing AI-Derived Knowledge for the Patient's Treatment -- 1.4.4 Fair Access to Available Services for Genetic Examinations and Interventions -- 1.5 Epidemiological Perspectives on Ethical AI Frameworks -- 1.5.1 AI and Disease Surveillance -- 1.5.2 Public Health Interventions and AI -- 1.5.3 Information Confidentiality Applying to Consent in Epidemiology Designs -- 1.5.4 Bias in Algorithmic Decision-Making -- 1.6 Ethical Frameworks for AI in Epidemiology -- 1.6.1 Principles of Transparency, Accountability, and Fairness -- 1.6.2 Responsible Use of AI-Powered Insights -- 1.6.3 Fair Division of AI Advantages Across Social Groups -- 1.7 Conclusion -- References -- Chapter 2 Ethical Challenges and Guidelines for AI Deployment in Healthcare: Urological and Gastroenterological Perspectives on Ethical AI Deployment -- 2.1 Introduction -- 2.1.1 Summary of AI in Medicine -- 2.1.2 Importance of Ethical Considerations in AI Deployment -- 2.2 Ethical Principles in AI Deployment. ; 2.2.1 Principles of Beneficence, Nonmaleficence, Autonomy, and Justice -- 2.2.2 Explainability and Transparency in AI Algorithms -- 2.2.3 The Importance of Accountability and Responsibility in AI Decision-Making -- 2.3 Challenges in AI Deployment in Urology and Gastroenterology -- 2.3.1 Data Privacy and Security Concerns -- 2.3.2 Bias and Fairness in AI Algorithms -- 2.3.3 Clinical Integration and Acceptance of AI Technologies -- 2.4 Guidelines for Ethical AI Deployment in Urology and Gastroenterology -- 2.4.1 Data Governance and Management -- 2.4.2 Safeguarding Patient Consent and Sensitive Information -- 2.4.3 Addressing Bias within AI Algorithms -- 2.4.4 Clinical Validation and Evaluation of AI Technologies -- 2.5 Case Studies -- 2.5.1 Application of AI Technology in Urology with Regard to Chronic Prostate Cancer -- 2.5.2 The Role of AI in Gastroenterology, with Relation to Diagnosing Other Digestive Tract Ailments -- 2.6 Future Directions and Recommendations -- 2.6.1 Progress of AI Ethics and Regulation -- 2.6.2 Collaboration Between Stakeholders for Ethical AI Deployment -- 2.6.3 Continuous Monitoring and Evaluation of AI Technologies -- 2.7 Conclusion -- References -- Chapter 3 Bias Mitigation and Fairness in AI Healthcare Applications: Addressing Bias and Equity in AI-Driven Healthcare Solutions -- 3.1 Introduction -- 3.1.1 AI in Healthcare -- 3.1.2 Bias Mitigation and Fairness -- 3.2 Bias in AI Healthcare Applications -- 3.2.1 Sources of Bias in AI Algorithms -- 3.2.2 Impact of Bias on Healthcare Equity -- 3.3 Strategies for Bias Mitigation in AI Healthcare -- 3.3.1 Diverse and Representative Training Data -- 3.3.2 Designing Algorithms in a Clear Manner -- 3.3.3 Auditing and Measuring for Fairness -- 3.4 Promoting Equity in AI Healthcare -- 3.4.1 Accessibility of AI Technologies -- 3.4.2 Addressing Unique Needs of Marginalized Communities. ; 3.4.3 Designing for Inclusivity -- 3.5 Case Studies and Examples -- 3.5.1 Real-World Examples of Bias in Healthcare AI -- 3.5.2 Effective Approaches for Equity Promotion and Bias Mitigation -- 3.6 Future Directions and Challenges -- 3.6.1 Emerging Trends in Bias Mitigation -- 3.6.2 Ethical and Legal Considerations -- 3.7 Conclusion -- References -- Chapter 4 Regulatory Compliance and Data Governance in AI-Driven Healthcare: Legal and Regulatory Considerations for AI-Driven Healthcare Solutions -- 4.1 Introduction -- 4.1.1 An Overview of Healthcare Options Based on AI -- 4.1.1.1 Benefits of AI-Driven Healthcare Solutions -- 4.1.1.2 AI-Driven Solutions in Healthcare- Implementation Challenges -- 4.1.2 Importance of Compliance with Regulations and Governance of Data -- 4.2 Legal and Regulatory Frameworks -- 4.2.1 Health Insurance Portability and Accountability Act -- 4.2.2 General Data Protection Regulation -- 4.2.3 National Laws and Guidelines -- 4.3 Data Governance in AI-Driven Healthcare -- 4.3.1 Establishing Data Governance Frameworks -- 4.3.2 Ensuring Data Quality, Security, and Privacy -- 4.3.3 Ethical Considerations and Best Practices -- 4.4 Regulatory Compliance Challenges -- 4.4.1 Sensitive Patient Data -- 4.4.1.1 Challenges of Regulatory Compliance -- 4.4.1.2 Techniques for Resolving Compliance Issues-Regulations -- 4.4.2 Owning Data: The Legal Maze -- 4.4.3 Assigning Responsibility for AI Deficiencies and Defaults -- 4.5 Ethical Guidelines for AI in Healthcare -- 4.5.1 European Commission's Ethical Guidelines for Trustworthy AI -- 4.5.2 Ethical Considerations in AI Algorithm Design and Deployment -- 4.6 Case Studies -- 4.6.1 Successful Implementation of Data Governance Frameworks -- 4.6.2 Challenges Faced and Lessons Learned -- 4.7 Future Trends and Considerations -- 4.7.1 Emerging Regulatory Trends in AI-Driven Healthcare. ; 4.7.2 Possible Effects of New Technologies on Fulfilling Regulatory Obligations -- 4.8 Conclusion -- References -- Chapter 5 Ensuring Responsible Data Use in Healthcare AI Applications: Radiological and Surgical Approaches to Responsible AI Data Usage -- 5.1 Introduction -- 5.1.1 Overview of Healthcare AI Applications -- 5.1.2 Significance on Responsible Use of Data -- 5.2 Responsible Data Use in Radiological AI Applications -- 5.2.1 Role of AI in Radiological Imaging -- 5.2.2 Data Privacy and Anonymization -- 5.2.3 Consent Management for AI Data Usage -- 5.2.4 Strategies for Addressing Bias -- 5.2.5 Transparency and Monitoring in AI Algorithms -- 5.3 Responsible Data Use in Surgical AI Applications -- 5.3.1 Utilizing AI in the Preoperative Planning and Surgical Decision-Making Process -- 5.3.2 Data Security and Patient Privacy in Surgical AI -- 5.3.3 Data Security: Encryption and Protection Measures -- 5.3.4 Methods of Ensuring Secure Transmission of Data -- 5.3.5 Use of AI in Interpretable Algorithms for Surgery -- 5.4 Multidisciplinary Approaches to Responsible AI Data Usage -- 5.4.1 Collaboration Between Radiologists, Surgeons, and Data Scientists -- 5.4.2 Ethical Considerations in AI Development -- 5.4.3 Compliance with Regulatory Frameworks -- 5.5 Case Studies and Best Practices -- 5.5.1 Successful Implementations of Responsible Data Use in Healthcare AI -- 5.5.2 Recap of the Case Real-Life Use Studies -- 5.6 Radiological and Surgical Approaches to Responsible AI Data Usage -- 5.6.1 Radiological Approaches -- 5.6.2 Surgical Approaches -- 5.6.3 Collaboration and Compliance -- 5.7 Conclusion -- References -- Chapter 6 Implementing Secure Health Data Exchange with Blockchain: Orthopedic and Ophthalmological Insights into Secure Health Data Exchange -- 6.1 Introduction -- 6.1.1 Overview of Health Data Exchange. ; 6.1.2 Importance of Security in Health Data Exchange -- 6.1.3 Role of Blockchain Technology in Secure Health Data Exchange -- 6.2 Orthopedic Insights into Secure Health Data Exchange -- 6.2.1 Orthopedic Data Exchange Obstacles -- 6.2.2 Implementing Blockchain in the Sharing of Orthopedic Data -- 6.2.3 Case Studies and Stories of Success -- 6.3 Ophthalmological Insights into Secure Health Data Exchange -- 6.3.1 Challenges in Sharing Ophthalmological Information -- 6.3.2 Using Blockchain Technology for Data Sharing in Ophthalmology -- 6.3.3 Case Studies and Success Stories -- 6.4 Blockchain Technology for Health Data Exchange -- 6.4.1 Understanding Blockchain Technology -- 6.4.2 Advantages and Disadvantages of Blockchain Technology with Respect to Heath Data Exchange -- 6.4.2.1 Advantages of Applying Blockchain Technology in Health Data Exchange -- 6.4.2.2 Limitations of Blockchain in Health Data Exchange -- 6.5 Regulatory and Legal Considerations -- 6.5.1 HIPAA Compliance and Health Data Security -- 6.5.2 GDPR and Protection of Sensitive Health Information -- 6.5.3 Legal Implications of Blockchain in Health Data Exchange -- 6.6 Future Trends and Challenges -- 6.6.1 New Developments in Technological Health Data Exchange -- 6.6.2 Challenges and Opportunities in Implementing Blockchain -- 6.6.3 Future Directions for Secure Health Data Exchange -- 6.7 Conclusion -- References -- Chapter 7 Securing Clinical Trial Data with Decentralized Technologies and Exploring Blockchain Applications in Modern Healthcare Management -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Overview of Blockchain Technology -- 7.4 Methodology -- 7.5 Blockchain Applications in Clinical Trial Data Management -- 7.6 Decentralized Technologies in Healthcare Management -- 7.7 Results and Discussion -- 7.8 Conclusion -- References. ; Chapter 8 Blockchain-Enabled Healthcare Ecosystems: Scalability, Security, and Interoperability.
• Sommario/riassunto: 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.
• Titolo autorizzato: Artificial Intelligence and Machine Learning in Neurology, 2 Volume Set
• ISBN: 1-394-38912-4; 1-394-38911-6; 1-394-38913-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911054510903321
• Lo trovi qui: Univ. Federico II