Muutke küpsiste eelistusi

Scalable Artificial Intelligence for Healthcare: Advancing AI Solutions for Global Health Challenges [Pehme köide]

Edited by , Edited by , Edited by
  • Formaat: Paperback / softback, 154 pages, kõrgus x laius: 234x156 mm, kaal: 300 g, 37 Tables, black and white; 27 Halftones, black and white; 27 Illustrations, black and white
  • Sari: Analytics and AI for Healthcare
  • Ilmumisaeg: 05-May-2025
  • Kirjastus: CRC Press
  • ISBN-10: 1032769599
  • ISBN-13: 9781032769592
Teised raamatud teemal:
Scalable Artificial Intelligence for Healthcare: Advancing AI Solutions for Global Health ChallengesSuurem pilt
  • Formaat: Paperback / softback, 154 pages, kõrgus x laius: 234x156 mm, kaal: 300 g, 37 Tables, black and white; 27 Halftones, black and white; 27 Illustrations, black and white
  • Sari: Analytics and AI for Healthcare
  • Ilmumisaeg: 05-May-2025
  • Kirjastus: CRC Press
  • ISBN-10: 1032769599
  • ISBN-13: 9781032769592
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781032769592.html
Märksõnad:

This edited volume examines the transformative impact of AI technologies on global healthcare systems, with a focus on enhancing efficiency and accessibility. A comprehensive guide for healthcare professionals, AI researchers, and those seeking to develop effective AI-driven healthcare solutions that address global health challenges.



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.

Table of Contents

1. AI in Healthcare: Addressing Challenges and Enabling Transformation
Houneida Sakly, Ramzi Guetari, Naoufel Kraiem and Mourad Said

2. Fundamental Principles of AI Scalability in Healthcare
Abdallah Ahmed Wajdi, Houneida Sakly, Ramzi Guetari and Naoufel Kraiem

3. Architectures for Scalable AI in Healthcare
Houneida Sakly, Ramzi Guetari, Naoufel Kraiem and Mourad Abed

4. Big Data and AI Solutions for Transforming Healthcare: Frameworks, Challenges, and Future Directions
Houneida Sakly, Ramzi Guetari, Naoufel Kraiem and Mourad Abed

5. Scalable Machine Learning for Healthcare: Techniques, Applications, and Collaborative Frameworks
Alaa Eddinne ben hmida, Houneida Sakly, Ramzi Guetari and Naoufel Kraiem

6. Deployment and continuous integration of AI in healthcare
Houneida Sakly, Ramzi Guetari and Naoufel Kraiem

7. AI Performance Optimization for Healthcare
Houneida Sakly, Ramzi Guetari and Naoufel Kraiem

8. Scaling AI Capabilities and Establishing a Roadmap for Sustainable Growth in Healthcare
Houneida Sakly, Ramzi Guetari and Naoufel Kraiem

9. Governance, Lessons, and Future Trends for Scalable AI in Healthcare
Houneida Sakly, Ramzi Guetari, Naoufel Kraiem and Mourad Said

Houneida Sakly is an Assistant Professor at CRMN in Tunisias 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.