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

1. Introduction 1.1 Background and Motivation 1.2 Scope of the Study 1.3 Structure of the Study 2. Understanding AI in Healthcare 2.1 Role of AI in Biomedical Engineering 2.2 AI Applications in Psychiatry 2.3 Recent Advances in AI Diagnostics 3. Alzheimer’s Disease and Machine Learning 3.1 Diagnostic Processes of Alzheimer’s 3.2 AI Techniques in Alzheimer’s Detection 3.3 Potential Biases in Algorithms 4. Addiction Treatment and AI 4.1 Understanding Addiction and Psychiatry 4.2 AI’s Role in Treatment Plans 4.3 Biases in AI-Based Interventions 5. Identifying AI-Induced Biases 5.1 Types of Biases in AI Systems 5.2 Sources of Bias in Medical AI 5.3 Case Studies on Bias 6. Mitigating Biases in AI Systems 6.1 Bias Detection Techniques 6.2 Fairness in AI Models 6.3 Ethical Considerations 7. Evaluating AI Outcomes in Diagnosis and Treatment 7.1 Metrics for AI Performance 7.2 Comparing AI and Human Decisions 7.3 Clinical Trial Evaluations 8. Conclusion and Future Directions 8.1 Summary of Findings 8.2 Implications for Biomedical Practice 8.3 Recommendations for Future Research