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Tiny Machine Learning (TinyML) offers a transformative approach to efficient, intelligent cancer diagnostics on edge devices. However, designing optimized TinyML models for breast cancer diagnosis using Microwave Imaging datasets remains a challenge due to domain-specific customization requirements. To address this, we propose a novel Knowledge Distillation framework, where a teacher model, built using residual convolutional neural networks, transfers its knowledge to a lightweight student model, enhancing efficiency while maintaining high accuracy. The teacher model achieves a 95.42% accuracy on test data. Through knowledge distillation, the student model attains 95.32% accuracy while achieving a 96% reduction in model size, significantly enhancing computational efficiency without compromising performance. Without distillation, the student model reaches a testing accuracy of 86.5%. A major contribution of this work is the generation of a highquality synthetic breast cancer dataset to address the scarcity of microwave imaging data. We employ a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) to synthesize realistic breast cancer scans, ensuring reliability through rigorous validation with a one-class Support Vector Machine. By training all models on a combination of real and synthetic datasets, our approach enhances robustness, making TinyML-powered breast cancer detection more viable for realworld deployment. 

The accelerating impact of AI in biomedical research is driving significant advances in precision medicine. As these systems increasingly shape health outcomes, the imperative to develop trustworthy, reliable, and ethically grounded AI becomes more pressing, particularly in addressing concerns related to data integrity, patient safety, and equitable outcomes. While the potential of AI to transform biomedical research is clear, its responsible integration depends on more than technological capability. Ensuring that these systems are aligned with societal values requires a dual commitment: the operationalization of ethical principles throughout the AI life cycle and the establishment of robust regulatory mechanisms. Ethics provides the normative vision for fairness, accountability, and human dignity, whereas regulation translates these ideals into enforceable standards. This paper explores the convergence of these domains as a necessary foundation for developing trustworthy human-centered AI in biomedical contexts. We provide practical guidance for AI developers and researchers on integrating proactive governance and translating ethical principles into actionable strategies to support equitable and responsible innovation.