Scalable Artificial Intelligence for Healthcare: Advancing AI Solutions for Global Health Challenges
About this ebook
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.
About the author
Houneida Sakly is an Assistant Professor at CRMN in Tunisia’s Sousse Techno Park. Holding a Ph.D. from ENSI in partnership with French universities (Gustave Eiffel University –ESIEE-Paris and Polytech-Orléans), she specializes in data science applied to healthcare. She collaborates with Stanford and is certified by MIT-Harvard in healthcare innovation.
Ramzi Guetari is an Associate Professor of Computer Science at the Polytechnic School of Tunisia. He achieved his Ph.D. at the University of Savoie, France, worked at the INRIA, contributed to W3C standards, and now studies AI and machine learning, collaborating with international organizations and companies.
Naoufel Kraiem is a Full Professor of Computer Science with 32 years in academia. He earned his Ph.D. at the University of Paris 6 and Habilitation from Sorbonne University. His research spans IT, data science, and software engineering, supported by the CNRS, INRIA, and EU programs, with over 147 publications.
