This edited volume examines the transformative impact of AI technologies on global healthcare systems, with a focus on enhancing efficiency and accessibility. The content provides a comprehensive exploration of the principles and practices required to scale AI applications in healthcare, addressing areas such as diagnosis, treatment, and patient care.
Key topics include data scalability, model deployment, and infrastructure design, highlighting the use of microservices, containerization, cloud computing, and big data technologies in building scalable AI systems. Discussions cover advancements in machine learning models, distributed processing, and transfer learning, alongside critical considerations such as continuous integration, data privacy, and ethics. Real-world case studies depict both the successes and challenges of implementing scalable AI across various healthcare environments, offering valuable insights for future advancements.
This volume serves as a practical and theoretical guide for healthcare professionals, AI researchers, and technology enthusiasts seeking to develop or expand on AI-driven healthcare solutions to address global health challenges effectively.
