Scalable Artificial Intelligence for Healthcare (eBook, ePUB)

Advancing AI Solutions for Global Health Challenges
Redaktion: Sakly, Houneida; Kraiem, Naoufel; Guetari, Ramzi

• Format: ePub

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis eBooks
• Erscheinungstermin: 6. Mai 2025
• Englisch
• ISBN-13: 9781040349847
• Artikelnr.: 73747347

Autorenporträt

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.

Inhaltsangabe

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