Investigating AI-Induced Biases in Diagnosing and Treating Alzheimer’s Disease in Biomedical Engineering

1. Introduction 1.1 Background and Motivation 1.2 Objectives of the Study 1.3 Structure of the Thesis 2. Overview of Artificial Intelligence in Healthcare 2.1 AI Technologies in Medicine 2.2 Applications of AI in Neurology 2.3 Benefits and Challenges of AI 3. Understanding Bias in AI Systems 3.1 Definition and Types of Bias 3.2 Sources of AI Bias 3.3 Impact of Bias in Decision-Making 4. Alzheimer’s Disease: Diagnosis and Treatment 4.1 Pathophysiology of Alzheimer’s Disease 4.2 Current Diagnostic Methods 4.3 Treatment Options and Their Limitations 5. AI Applications in Alzheimer’s Disease 5.1 Machine Learning Models for Diagnosis 5.2 AI in Personalized Treatment Plans 5.3 Case Studies of AI Implementation 6. Identifying AI-Induced Biases 6.1 Bias Detection Methods 6.2 Analyzing Bias in Diagnostic Tools 6.3 Bias in Treatment Algorithms 7. Mitigating Bias in AI Systems 7.1 Strategies to Reduce Bias 7.2 Ethical Considerations and Guidelines 7.3 Future Directions for Fair AI 8. Conclusion 8.1 Summary of Findings 8.2 Implications for Biomedical Engineering 8.3 Recommendations for Further Research