Lightweight, Real-time Deep Learning Models for Healthcare Applications

Lightweight, Real-time Deep Learning Models for Healthcare Applications addresses the pressing need for deploying efficient artificial intelligence models in clinical environments constrained by limited computational resources. As AI adoption accelerates in healthcare, this reference targets the gap between advanced deep learning research and practical real-time applications, emphasizing model optimization, privacy, and regulatory compliance. The content comprehensively covers model compression techniques such as pruning, quantization, knowledge distillation, and neural architecture search, alongside federated learning frameworks and real-time inference optimization. Detailed chapters present hardware-aware strategies, security considerations, continuous learning, and regulatory validation. The book also provides extensive case studies and implementation blueprints demonstrating practical deployment on devices like Raspberry Pi, NVIDIA Jetson, and mobile health platforms. This book benefits researchers, clinicians, and healthcare technology professionals by offering practical solutions to develop, validate, and implement AI models that maintain diagnostic accuracy while meeting clinical constraints. It equips students and educators with theoretical foundations and hands-on examples to foster innovation in biomedical AI, ultimately enhancing diagnostic capabilities and patient care in diverse healthcare settings.