Word count: 3500 words
Objectives to cover:
Introduction: Overview of diabetic retinopathy and the importance of early screening.
AI in Medical Imaging: The growing role of artificial intelligence in healthcare diagnostics.
AI Techniques in DR Detection: Overview of algorithm types used in diabetic retinopathy screening.
Diagnostic Accuracy Comparison: Analysis of performance across various AI models.
Validation and Datasets: Review of benchmark datasets and model validation studies.
Clinical Integration: Challenges and opportunities in implementing AI tools in practice.
Ethical and Regulatory Aspects: Examination of legal, ethical, and policy considerations.
Cost and Accessibility: Assessment of AI screening in low-resource and underserved settings.
Conclusion: Summary of findings, current limitations, and future research directions.
Reference: IEEE style