Table of Contents
Section I: Introduction to Artificial Intelligence
1. Artificial Intelligence in Healthcare
Section II: Data Science and Artificial Intelligence in Healthcare
2. Machine and Deep Learning Section III: Essential AI in Healthcare Topics
3. Ten Key Technologies in Healthcare AI
4. Ten Essential Dimensions in Healthcare AI
5. Ten Human-Related Topics in the Era of AI
6. AI in Healthcare: Lessons Learned and Future Trends
Section IV: AI in Healthcare Resources
7. Organizational AI Readiness Assessment
Section V: AI in Healthcare Case Applications
8. Case Study 1: Addressing Organizational Structures to Deploy an AI-Ready, Enterprise Scale Data Architecture
9. Case Study 2: Implementing Machine Learned Algorithms to Predict Patient Deterioration in Hospital
10. Case Study 3: Artificial Intelligence Applications for the Prevention, Diagnosis, and Management of Intraoperative Hypotension
11. Case Study 4: A Machine Learning-Based Risk Calculator for Personalized BPD Care: Predicting Readmission Risk
12. Case Study 5: Clinical Documentation Improvements Following Introduction of AI-Enabled Software
13. Case Study 6: Development, Deployment, and Maintenance of a Tool for Predicting Hospital Inpatient Census
14. Case Study 7: Supply-Eye: AI System that Automates the Tracking of Clinical Supplies Usage and Reordering
15. Case Study 8: Accuracy Improvement through the Development and Implementation of an Artificial Intelligence-Enabled Insurance Verification System
16. Case Study 9: Predicting Extubation Success in Patients with Established Bronchopulmonary Dysplasia
17. Case Study 10: Stroke Treatment and Outcomes at a Comprehensive Stroke Center Before and After Automated Emergent Large Vessel Occlusion Detection by Artificial Intelligence
18. Case Study 11: Refocusing Predictive Modeling on Diagnostic Decision-Making Support
19. Case Study 12: Leveraging a Large Language Model to Provide Clinical Documentation, Coding, and Quality Metric Extraction at Scale in Emergency Departments and Urgent Care
20. Case Study 13: Audio and Immersion
Text-to-Speech within Immersive Healthcare
21. Case Study 14: Deploying Ambient AI Scribes to Enhance Clinical Documentation and Reduce Provider Burnout
22. Case Study 15: Distributed Multi-Agent AI Application for Clinical Trial Recruiting
23. Case Study 16: Agentic AI with Practical Healthcare Applications
24. Case Study 17: Evaluation of Artificial Intelligence’s Impact on Patient Outcomes: The CLOT (Children’s Likelihood Of Thrombosis) Demonstration Project
Authors
Anthony Chang Director, UChicago Medical Laboratories, Director, Renal Pathology and Renal Pathology Fellowship, Associate Director, Pathology Residency Program, University of Chicago, Chicago, Illinois, USA. Dr. Anthony C. Chang is an active pediatric cardiologist and currently serves as Chief Intelligence and Innovation Officer at Rady Children's Health. A pioneer in healthcare AI, he founded the American Board of AI in Medicine (ABAIM) and chairs the Alliance of Centers of AI in Medicine (ACAIM). Alfonso Limon Principal, Oneirix Labs, USA.Dr. Alfonso Limon is the Senior Data Scientist at Mi4 within Rady Children's Health, specializing in AI and healthcare innovation. Previously served as a Principal at Oneirix Labs, a consulting firm specializing in computational intelligence for medical technology. Dr. Limon was Research Director at Intersection Medical (I-Med), where he developed decision-support algorithms for congestive heart failure, and previously managed the research team at Impedance Cardiology Systems. He serves as associate editor for Intelligence-Based Medicine, is a founding member of the American Board of AI in Medicine.
Gregg M. Gascon OhioHealth, USA.Dr. Gregg M. Gascon is a data science advisor at OhioHealth facilitating artificial intelligence implementation, evaluation, and optimization. Dr. Gascon is an assistant adjunct professor of Biomedical Informatics in The Ohio State University's College of Medicine and serves on the American Statistical Association's Scientific and Public Affairs Advisory Committee, the American Board of Artificial Intelligence in Medicine, and the International AI in Medicine Education Working Group at the University of Toronto.
