Intelligence-Based Healthcare: An Essential Guide with Case Studies of Artificial Intelligence for the Healthcare Leader and Provider
Intelligence-Based Healthcare: An Essential Guide with Case Studies of Artificial Intelligence for the Healthcare Leader and Provider serves as an indispensable resource for anyone seeking to understand and apply artificial intelligence (AI) within the healthcare sector. The book opens with a concise introduction to data science and AI concepts, providing a foundation for readers to quickly grasp the core principles involved. By blending practical examples and theoretical frameworks, it guides healthcare leaders and providers through the complexities of adopting AI-driven solutions. This guide is designed to orient readers rapidly, preparing them to engage with AI’s transformational potential in clinical and operational settings.

In addition to its comprehensive overview, the book presents a clear framework for successfully planning, deploying, implementing, and evaluating AI models in healthcare environments. Each of its 25 chapters can be read independently, making it a versatile tool for self-paced learning. The use of real-world case studies—both successful and unsuccessful—offers valuable insights and important lessons learned from practical applications. This approach not only demystifies AI but also illustrates its real impact on solving clinical and operational challenges.
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Intelligence-Based Healthcare: An Essential Guide with Case Studies of Artificial Intelligence for the Healthcare Leader and Provider
Intelligence-Based Healthcare: An Essential Guide with Case Studies of Artificial Intelligence for the Healthcare Leader and Provider serves as an indispensable resource for anyone seeking to understand and apply artificial intelligence (AI) within the healthcare sector. The book opens with a concise introduction to data science and AI concepts, providing a foundation for readers to quickly grasp the core principles involved. By blending practical examples and theoretical frameworks, it guides healthcare leaders and providers through the complexities of adopting AI-driven solutions. This guide is designed to orient readers rapidly, preparing them to engage with AI’s transformational potential in clinical and operational settings.

In addition to its comprehensive overview, the book presents a clear framework for successfully planning, deploying, implementing, and evaluating AI models in healthcare environments. Each of its 25 chapters can be read independently, making it a versatile tool for self-paced learning. The use of real-world case studies—both successful and unsuccessful—offers valuable insights and important lessons learned from practical applications. This approach not only demystifies AI but also illustrates its real impact on solving clinical and operational challenges.
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Intelligence-Based Healthcare: An Essential Guide with Case Studies of Artificial Intelligence for the Healthcare Leader and Provider

Intelligence-Based Healthcare: An Essential Guide with Case Studies of Artificial Intelligence for the Healthcare Leader and Provider

Intelligence-Based Healthcare: An Essential Guide with Case Studies of Artificial Intelligence for the Healthcare Leader and Provider
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Intelligence-Based Healthcare: An Essential Guide with Case Studies of Artificial Intelligence for the Healthcare Leader and Provider

Intelligence-Based Healthcare: An Essential Guide with Case Studies of Artificial Intelligence for the Healthcare Leader and Provider

Overview

Intelligence-Based Healthcare: An Essential Guide with Case Studies of Artificial Intelligence for the Healthcare Leader and Provider serves as an indispensable resource for anyone seeking to understand and apply artificial intelligence (AI) within the healthcare sector. The book opens with a concise introduction to data science and AI concepts, providing a foundation for readers to quickly grasp the core principles involved. By blending practical examples and theoretical frameworks, it guides healthcare leaders and providers through the complexities of adopting AI-driven solutions. This guide is designed to orient readers rapidly, preparing them to engage with AI’s transformational potential in clinical and operational settings.

In addition to its comprehensive overview, the book presents a clear framework for successfully planning, deploying, implementing, and evaluating AI models in healthcare environments. Each of its 25 chapters can be read independently, making it a versatile tool for self-paced learning. The use of real-world case studies—both successful and unsuccessful—offers valuable insights and important lessons learned from practical applications. This approach not only demystifies AI but also illustrates its real impact on solving clinical and operational challenges.

Product Details

ISBN-13: 9780443339332
Publisher: Elsevier Science
Publication date: 06/01/2026
Series: Intelligence-Based Medicine: Subspecialty Series
Pages: 350
Product dimensions: 7.50(w) x 9.25(h) x (d)

About the Author

Anthony Chang, MD, is a Professor, Department of Pathology, University of Chicago with a specialty in renal pathology. His research interests include the role of B and plasma cells in lupus nephritis and transplant rejection. He is past president of the Renal Pathology Society (2017) and Chicago Pathology Society (2011-2013). He has taught more than 30 educational courses at the annual meetings for the American Society Clinical Pathology, College of American Pathologists, US & Canadian Academy of Pathology, American Society of Nephrology, American College of Rheumatology, and American Urological Association.



Alfonso Limon, Ph.D., is a principal at Oneirix, a consulting company developing market-leading technologies in computational intelligence for med-tech. Before joining Oneirix, Dr. Limon served as Director of Research at Intersection Medical, leading the development of algorithms for decision support systems to manage congestive heart failure. Before his work in industry, Dr. Limon was a Visiting Professor of Mathematics at Pomona College and a post-doctoral fellow at Harvey Mudd College in the math department and holds several impedance spectroscopy patents. Alfonso is part of the American Board of Artificial Intelligence in Medicine, an Associate Editor of Intelligence-Based Medicine, and the Computational Science Research Center Board Chair at SDSU.

Table of Contents

1. Introduction
2. Brief History of Artificial Intelligence
3. Reflection and Update on AI in Medicine and Healthcare
4. Topics
5. Definitions
6. Machine Learning Supervised, Unsupervised, Semi- and Self-Supervised
7. Deep Learning Neural Nets and Transformers
8. Reinforcement Learning
9. Generative Artificial Intelligence
10. Natural Language Processing
11. Cognitive Computing
12. Robotic Process Automation
13. Regulation, Ethics, Adoption, and Legal (REAL) Issues
14. Implementation Science
15. Miscellaneous Topics
16. Why Adoption of AI in Healthcare Has Been Challenging
17. Framework for Successful Adoption of AI in Healthcare
18. How to Pursue a Basic to Advanced Education on Artificial Intelligence
19. How to Build Data, IT, and AI Infrastructure
20. How to Start a Center of Artificial Intelligence
21. Future of Artificial Intelligence and Health
22. Compendium
23. Glossary
24. Top 100 Articles of AI in Healthcare
25. Book Recommendations

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