Efficient Cost Aware Artificial Intelligence in Healthcare and Biomedicine is situated within the broader context of artificial intelligence (AI) and deep learning (DL) technologies as they pertain to healthcare and biomedicine. AI and DL have witnessed remarkable advancements in recent years, leading to breakthroughs in various applications, including medical image analysis, drug discovery, disease diagnosis, and personalized treatment recommendations. In 14 chapters this book offers techniques and best practices for compressing deep learning models, allowing them to run efficiently on healthcare devices with limited memory and processing power. This book provides the void code-first approach to optimizing AI for healthcare and biomedicine by offering practical solutions to the unique challenges posed by AI in healthcare. The growing demand for efficient AI computing techniques, coupled with advancements in Deep Learning and Large Language Models tailored for healthcare and biomedicine, makes this book, Efficient Cost Aware Artificial Intelligence in Healthcare and Biomedicine timely and essential. It addresses the pressing need for practical guidance on compressing and optimizing AI models for everyday healthcare devices, making it accessible to a broad and diverse audience seeking to harness the power of AI within the healthcare and biomedicine sectors. "Efficient AI in Healthcare and Biomedicine" empowers healthcare professionals, researchers, and organizations with practical solutions to the challenges posed by resource-intensive deep learning models in healthcare and biomedicine.

• provides healthcare-specific implementation guides and code examples, offering readers practical insights into optimizing AI models for medical imaging, patient diagnosis, and healthcare analytics
• emphasizes cost-effectiveness in the context of healthcare, illustrating how AI can be optimized to deliver high-quality care while minimizing the financial burden on healthcare organizations
• includes interviews with industry leaders, clinicians, and researchers specializing in healthcare AI, who share their practical experiences and insights into optimizing AI models for medical applications