ABSTRACT
This chapter bridges the gap between experimental artificial intelligence (AI) research and practical healthcare deployment through mitigation strategies, real-world validation, and policy-oriented recommendations. A case study from rural Maharashtra demonstrates the successful application of the proposed SegNet–Attention-Based CNN (ABCNN) model within a teledermatology framework, achieving dermatologist-level accuracy (98.9%) and rapid inference (< 2 seconds). Technical remedies—including dataset augmentation, artifact removal, dual attention mechanisms, and federated learning—improved model robustness, interpretability, and privacy compliance. Operational enhancements such as model quantization reduced latency by 40%, enabling real-time use on mobile and edge devices. The chapter further emphasizes ethical AI governance, fairness across skin tones, and integration with India’s Ayushman Bharat Digital Health Mission (ABDM). Policy frameworks for national AI certification, open data repositories, and capacity-building are outlined. Collectively, these measures position AI as a transformative, equitable, and sustainable solution for global dermatological healthcare.
