Scalable Artificial Intelligence For Healthcare
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Scalable Artificial Intelligence for Healthcare
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.
The Impact of Artificial Intelligence on Healthcare Industry
Healthcare and medical science are inherently dependent on technological advances and innovations for improved care. In recent times we have witnessed a new drive in implementing these advances and innovations through the use of Artificial Intelligence, in both clinical and non-clinical areas. The set of 2 volumes aims to make available the latest research and applications to all, and to present the current state of clinical and non-clinical applications in the health sector and areas open to development, as well as to provide recommendations to policymakers. This volume covers non-clinical applications. The chapters covered in this book have been written by professionals who are experts in the healthcare sector and have academic experience.
Federated Learning and AI for Healthcare 5.0
The Healthcare sector is evolving with Healthcare 5.0, promising better patient care and efficiency. However, challenges like data security and analysis arise due to increased digitization. Federated Learning and AI for Healthcare 5.0 offers solutions, explaining cloud computing's role in managing data and advocating for security measures. It explores federated learning's use in maintaining data privacy during analysis, presenting practical cases for implementation. The book also addresses emerging tech like quantum computing and blockchain-based services, envisioning an innovative Healthcare 5.0. It empowers healthcare professionals, IT experts, and data scientists to leverage these technologies for improved patient care and system efficiency, making Healthcare 5.0 secure and patient centric.