Intelligence-Based Healthcare

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). 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.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.

• Section I: Introduction to Artificial Intelligence1. Artificial Intelligence in HealthcareSection II: Data Science and Artificial Intelligence in Healthcare2. Machine and Deep Learning Section III: Essential AI in Healthcare Topics3. Ten Key Technologies in Healthcare AI4. Ten Essential Dimensions in Healthcare AI5. Ten Human-Related Topics in the Era of AI6. AI in Healthcare: Lessons Learned and Future TrendsSection IV: AI in Healthcare Resources7. Organizational AI Readiness AssessmentSection V: AI in Healthcare Case Applications8. Case Study 1: Addressing Organizational Structures to Deploy an AI-Ready, Enterprise Scale Data Architecture9. Case Study 2: Implementing Machine Learned Algorithms to Predict Patient Deterioration in Hospital10. Case Study 3: Artificial Intelligence Applications for the Prevention, Diagnosis, and Management of Intraoperative Hypotension11. Case Study 4: A Machine Learning-Based Risk Calculator for Personalized BPD Care: Predicting Readmission Risk12. Case Study 5: Clinical Documentation Improvements Following Introduction of AI-Enabled Software13. Case Study 6: Development, Deployment, and Maintenance of a Tool for Predicting Hospital Inpatient Census14. Case Study 7: Supply-Eye: AI System that Automates the Tracking of Clinical Supplies Usage and Reordering15. Case Study 8: Accuracy Improvement through the Development and Implementation of an Artificial Intelligence-Enabled Insurance Verification System16. Case Study 9: Predicting Extubation Success in Patients with Established Bronchopulmonary Dysplasia17. Case Study 10: Stroke Treatment and Outcomes at a Comprehensive Stroke Center Before and After Automated Emergent Large Vessel Occlusion Detection by Artificial Intelligence18. Case Study 11: Refocusing Predictive Modeling on Diagnostic Decision-Making Support19. Case Study 12: Leveraging a Large Language Model to Provide Clinical Documentation, Coding, and Quality Metric Extraction at Scale in Emergency Departments and Urgent Care20. Case Study 13: Audio and Immersion – Text-to-Speech within Immersive Healthcare21. Case Study 14: Deploying Ambient AI Scribes to Enhance Clinical Documentation and Reduce Provider Burnout22. Case Study 15: Distributed Multi-Agent AI Application for Clinical Trial Recruiting23. Case Study 16: Agentic AI with Practical Healthcare Applications24. Case Study 17: Evaluation of Artificial Intelligence’s Impact on Patient Outcomes: The CLOT (Children’s Likelihood Of Thrombosis) Demonstration Project