The integration of artificial intelligence (AI) in healthcare has significantly advanced diagnostic capabilities, particularly in medical imaging. However, the computational complexity of ensemble models poses challenges for deployment in resource-constrained environments. This concept paper explores the application of knowledge distillation to develop lightweight models that retain high predictive performance while reducing computational overhead. The proposed framework incorporates scalability, efficiency, and ethical considerations, addressing data privacy, fairness, and interpretability. Methods to enhance explainability, such as gradient-based visualizations, are integrated to build trust in clinical decision-making. By synthesizing insights from existing literature, this work establishes a foundation for future empirical research and outlines pathways for deploying AI-driven diagnostics in underserved regions.