Intelligence-Based Cardiology and Cardiac Surgery
Artificial Intelligence and Human Cognition in Cardiovascular Medicine
- 1st Edition - September 6, 2023
- Imprint: Academic Press
- Editors: Anthony C. Chang, Alfonso Limon, Robert Brisk, Francisco Lopez- Jimenez, Louise Y Sun
- Language: English
- Hardback ISBN:9 7 8 - 0 - 3 2 3 - 9 0 5 3 4 - 3
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 0 6 2 9 - 6
Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine provides an especially timely multidisciplinary and compre… Read more
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Request a sales quoteIntelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine provides an especially timely multidisciplinary and comprehensive survey of artificial intelligence concepts and methodologies. It includes real-life applications in adult and pediatric cardiovascular medicine, spanning the life span from fetus to adult. Led by a senior cardiologist–data scientist and supported by renowned data scientists and cardiac clinicians with an ardent passion for artificial intelligence in cardiovascular medicine, the book provides a clinical interface between the medical and data science domains that is symmetric and realistic.
The content consists of basic concepts and applications of artificial intelligence and human cognition in cardiology and cardiac surgery. This portfolio ranges from big data to machine and deep learning, as well as cognitive computing and natural language processing in cardiac disease states such as heart failure, hypertension, and pediatric cardiac care. Artificial intelligence tools are described from the intensive care unit setting to other venues, such as the outpatient clinic, catheterization laboratory, and operating room. Future applications in related areas, such as large language models, extended reality, and digital twins, are also discussed. The book encompasses more than 50 chapters written by cardiologists or cardiac surgeons. Each chapter provides sections on the current state of the art and future directions and concludes with major takeaways. A robust compendium of practical resources, such as a comprehensive glossary, best references, and other resources, is also included.
The book narrows the knowledge and expertise chasm between data scientists, cardiologists, and cardiac surgeons, inspires these clinicians to embrace artificial intelligence methodologies, and educates data scientists about the cardiac ecosystem to create a transformational paradigm for cardiovascular healthcare that improves patient outcomes.
The content consists of basic concepts and applications of artificial intelligence and human cognition in cardiology and cardiac surgery. This portfolio ranges from big data to machine and deep learning, as well as cognitive computing and natural language processing in cardiac disease states such as heart failure, hypertension, and pediatric cardiac care. Artificial intelligence tools are described from the intensive care unit setting to other venues, such as the outpatient clinic, catheterization laboratory, and operating room. Future applications in related areas, such as large language models, extended reality, and digital twins, are also discussed. The book encompasses more than 50 chapters written by cardiologists or cardiac surgeons. Each chapter provides sections on the current state of the art and future directions and concludes with major takeaways. A robust compendium of practical resources, such as a comprehensive glossary, best references, and other resources, is also included.
The book narrows the knowledge and expertise chasm between data scientists, cardiologists, and cardiac surgeons, inspires these clinicians to embrace artificial intelligence methodologies, and educates data scientists about the cardiac ecosystem to create a transformational paradigm for cardiovascular healthcare that improves patient outcomes.
- Covers a wide range of relevant topics from real-world data, large language models, and supervised machine learning to deep reinforcement and federated learning
- Presents artificial intelligence concepts and their applications in many areas in an easy-to-understand format accessible to clinicians and data scientists
- Discusses using artificial intelligence and related technologies with cardiology and cardiac surgery in a myriad of venues and situations
- Delineates the necessary elements for successfully implementing artificial intelligence in cardiovascular medicine for improved patient outcomes
- Presents the regulatory, ethical, legal, and financial issues embedded in artificial intelligence applications in cardiology
Cardiologists (adult and pediatric), Cardiac surgeons (adult and pediatric), Cardiac anesthesiologists, Cardiac intensivists, Trainees in these fields (adult and pediatric), Nurses and technologists in these fields, Health executives, Data scientists (primary), Inventors, Entrepreneurs
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Contributors
- About the editors
- Foreword by Eric Topol
- Foreword by Ami Bhatt
- Preface
- Acknowledgments
- Section I. Basic concepts of data science and artificial intelligence
- Chapter 1. Introduction to artificial intelligence for cardiovascular clinicians
- Basic concepts of artificial intelligence
- History of artificial intelligence
- History of artificial intelligence in medicine
- Healthcare data and databases
- Machine and deep learning
- Assessment of model performance
- Fundamental issues in machine and deep learning
- Other key concepts and technologies in artificial intelligence
- Human cognition and artificial intelligence in cardiology
- Current status of AI in medicine and relevance to cardiovascular medicine
- Artificial intelligence in cardiovascular medicine
- Subsection A. Basic concepts of artificial intelligence in cardiology and cardiac surgery
- Chapter 2. Application of artificial intelligence in cardiovascular medicine and cardiac surgery
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 3. Data and databases in cardiovascular medicine and surgery
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 4. Data and databases for pediatric and adult congenital cardiac care
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 5. Cognitive biases and heuristics in human cognition
- Introduction
- Major Takeaways
- Chapter 6. Spectrum bias in algorithms and artificial intelligence
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 7. Medical visual question answering
- Introduction
- Current state of the art
- Future directions
- Main takeaways
- Subsection B. Artificial intelligence in cardiovascular areas
- Chapter 8. Artificial intelligence and the electrocardiogram
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 9. Artificial intelligence in electrophysiology
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 10. Artificial intelligence in echocardiography
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 11. Artificial intelligence in cardiac CT
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 12. Artificial intelligence in cardiac MRI
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 13. Artificial intelligence in pediatric and congenital cardiac magnetic resonance imaging
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 14. Artificial intelligence in three-dimensional and fetal echocardiography
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 15. Artificial intelligence in nuclear cardiology
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 16. Artificial intelligence and in situ exercise monitoring, modeling, and guidance
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 17. Artificial intelligence in the catheterization laboratory
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 18. Artificial intelligence in the cardiology clinic
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 19. Artificial intelligence in cardiac surgery
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 20. Congenital cardiac surgery and artificial intelligence
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Subsection C. Clinical applications of artificial intelligence in cardiovascular medicine
- Chapter 21. Artificial intelligence in heart failure
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 22. Big data in cardiovascular population health research
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 23. Intelligence-based cardiovascular disease prevention
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 24. Artificial intelligence in cardiovascular genetics
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 25. Artificial intelligence in congenital heart disease
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 26. Artificial intelligence and cardiovascular disease in women
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 27. Artificial intelligence and COVID-19 in children with heart disease
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 28. Artificial intelligence in cardiac critical care
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 29. Artificial intelligence in cardio-oncology
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 30. Artificial intelligence in adult congenital heart disease
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 31. Artificial intelligence for quality improvement
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 32. Clinical safety in cardiology and artificial intelligence
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Subsection D. Artificial intelligence and related technologies in cardiovascular medicine
- Chapter 33. Data sharing principles
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 34. Natural language processing in cardiovascular medicine
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 35. Artificial intelligence and wearable technology
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 36. Digital twin in cardiovascular medicine and surgery
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 37. Artificial intelligence and extended reality in cardiology
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 38. Cybersecurity and blockchain in cardiology
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Subsection E. Artificial intelligence and special topics in cardiovascular medicine
- Chapter 39. Starting an artificial intelligence program in cardiology
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 40. Artificial intelligence for cardiac care: a view from the top
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 41. Strategy of artificial intelligence in cardiology
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 42. Education of artificial intelligence for cardiovascular clinicians
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 43. Artificial intelligence in cardiology: the trainee perspective
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 44. Artificial intelligence and agile project management
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 45. Synthetic data in cardiovascular health research
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 46. Ethical and legal issues in artificial intelligence-based cardiology
- Introduction
- Current state of the art
- Major takeaways
- Chapter 47. Regulatory frameworks for artificial intelligence in cardiovascular medicine and surgery
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 48. Regulatory issues of artificial intelligence in cardiology – international perspective
- Introduction
- Current state of the art
- Future Directions
- Major Takeaways
- Chapter 49. Industry perspective of artificial intelligence in medicine and surgery
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 50. Entrepreneurship lessons from artificial intelligence in cardiology
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 51. Global cardiac network: an innovative artificial intelligence-enabled learning system
- Introduction
- Current state of the art
- Future directions
- Major takeaways
- Chapter 52. The future of artificial intelligence in cardiology and cardiac surgery
- Introduction
- The future of artificial intelligence in cardiology and cardiac surgery
- The future of artificial intelligence in cardiovascular medicine—stakeholders
- The future of dyads in artificial intelligence in cardiovascular medicine
- Major takeaways
- Suggested readings
- Section III Artificial intelligence in medicine compendium
- Appendix. Compendium
- Recommended resources
- Glossary
- Index
- Edition: 1
- Published: September 6, 2023
- No. of pages (Hardback): 540
- No. of pages (eBook): 260
- Imprint: Academic Press
- Language: English
- Hardback ISBN: 9780323905343
- eBook ISBN: 9780323906296
AC
Anthony C. Chang
Dr. Chang is the founder and medical director of the Medical Intelligence and Innovation Institute (MI3) that is supported by the Sharon Disney Lund Foundation. The institute is dedicated to the introduction and implementation of artificial intelligence in medicine and was the first institute of its kind in a hospital. Dr. Chang intends to build a clinician-computer scientist interface with a nascent society (the Medical Intelligence Society) and is the editor-in-chief of Intelligence-based Medicine, the accompanying journal for his book, Intelligence-Based Medicine: Artificial Intelligence and Human Cognition in Clinical Medicine and Healthcare. He is the organizing chair for Artificial Intelligence in Medicine (AIMed) meetings around the world, the largest and most comprehensive clinician-led meetings that focus on applications of artificial intelligence in medicine and the dean of the nascent American Board of Artificial Intelligence in Medicine (ABAIM). He is also the founding president of the Medical Intelligence Society (MIS).
Affiliations and expertise
Sharon Disney Lund Medical Intelligence, Information, Investigation, and Innovation Institute (Mi4), Children’s Health of Orange County, Orange, CA, USA; Heart Failure Program, Heart Institute, Children’s Health of Orange County, Orange, CA, USA; Chapman University, Orange, CA, USAAL
Alfonso Limon
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.
Affiliations and expertise
Principal, Oneirix Labs, USAFJ
Francisco Lopez- Jimenez
Dr. Lopez-Jimenez is a Professor of Medicine at Mayo Clinic College of Medicine, the Chair of the Division of Preventive Cardiology at Mayo Clinic and Co-Director of Artificial Intelligence in Cardiology in the Department of Cardiovascular Medicine. He is the Editor-In-Chief of Mayo Clinic Proceedings: Digital Health and the Co-Chair for the Advanced Healthcare Analytics workgroup, American College of Cardiology. Dr. Lopez-Jimenez did his cardiology fellowship at Mount Sinai Medical Center in Miami, Florida and at Brigham and Women’s Hospital, Harvard Medical School. He holds a Master of Science degree from Harvard School of Public Health and a MBA degree from Augsburg University. Dr. Lopez-Jimenez has published more than 365 scientific publications and his scientific work has been cited more than 17,000 times.
Affiliations and expertise
Professor of Medicine, Mayo Clinic College of Medicine, MN, USA
Chair of the Division of Preventive Cardiology, Mayo Clinic, MN, USA
Co-Director of Artificial Intelligence in Cardiology in the Department of Cardiovascular Medicine, USALY
Louise Y Sun
Dr. Sun is Professor and Chief of Cardiothoracic Anesthesiology at Stanford. Her areas of clinical focus are hemodynamic monitoring and heart failure. Her methodologic areas of focus are the conduct of population-based cohort studies, predictive analytics, sex and gender epidemiology, patient engagement, data warehousing, and applications development. Her patient-centered research program leverages big data and digital technology to bridge key gaps in the delivery of care and outcomes for patients with heart failure and those undergoing cardiovascular interventions, through personalized risk stratification and characterizing long-term, patient-defined outcomes. She specializes in rapidly developing and deploying data-driven solutions to enhance clinical operations and patient care, and collaborates with policy makers to evaluate models of cardiac healthcare delivery. Dr. Sun sits on a number of editorial boards and scientific review committees internationally. She has authored over 100 peer-reviewed publications, many in leading journals including JAMA, JAMA Cardiology, JAMA Internal Medicine, Circulation, JACC, and Diabetes Care.
Affiliations and expertise
Professor and Chief of Cardiothoracic Anesthesiologiy, Stanford University School of Medicine, Stanford, CA, USARead Intelligence-Based Cardiology and Cardiac Surgery on ScienceDirect