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Elsevier

Intelligence-Based Cardiology and Cardiac Surgery

Artificial Intelligence and Human Cognition in Cardiovascular Medicine

  • 1st Edition - September 6, 2023
  • Latest edition
  • Editors: Anthony C. Chang, Alfonso Limon, Robert Brisk, Francisco Lopez- Jimenez, Louise Y Sun
  • Language: English

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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Description

Intelligence-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.

Key features

  • 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

Readership

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

Table of contents

I. INTRODUCTION TO ARTIFICIAL INTELLIGENCE 1. Basic Concepts of Artificial Intelligence 2. History of Artificial Intelligence 3. History of Artificial Intelligence in Medicine II. DATA SCIENCE AND ARTIFICIAL INTELLIGENCE IN THE CURRENT ERA 4. Health Care Data and Databases 5. Machine and Deep Learning - Introduction to Machine Learning - Classical Machine Learning - Supervised Learning - Unsupervised Learning - SemiSupervised Learning - Ensemble Learning - Reinforcement Learning - Neural Networks and Deep Learning - Perceptron and Multilayer Perceptrons (MLP) - Deep Learning - AutoEncoder neural network - Generative adversarial network (GAN) - Convolutional neural network (CNN) - Recurrent neural network (RNN) - Assessment of Model Performance - Assessment Methods - Evaluation of Regression Models - Evaluation of Classification Models - Confusion matrix - Receiver operating characteristic (ROC) - Precision recall curve (PRC) - Fundamental Issues in Machine and Deep Learning 6. Other Key Concepts in Artificial Intelligence - Cognitive Computing - Natural Language Processing (NLP) - Robotics - Other Key Technologies Related to Artificial Intelligence - Key Issues Related to Artificial Intelligence - Ten Questions to Assess Your Data Science and Artificial Intelligence Knowledge - Ten Steps to Become More Knowledgeable in Artificial Intelligence in Medicine III. THE CURRENT ERA OF ARTIFICIAL INTELLIGENCE IN MEDICINE 7. Clinician Cognition and Artificial Intelligence in Medicine - The “Why” for Intelligence-Based Medicine: Sanctuary for the Perfect Storm - Adoption of AI in Medicine: The Challenges Ahead - Clinicians and Human Cognition - Current AI in Medicine Applications - Medical Imaging - Altered Reality - Decision Support - Biomedical Diagnostics - Precision Medicine - Drug Discovery - Digital Health - Wearable Technology - Robotic Technology - Virtual Assistance

Product details

  • Edition: 1
  • Latest edition
  • Published: September 7, 2023
  • Language: English

About the editors

AC

Anthony C. Chang

Dr. Chang is the founder and medical director of the Medical Intelligence, Information, Investigation, and Innovation Institute (Mi4) 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 Hospital of Orange County, a part of Rady Children’s Health, Orange, CA, USA; Heart Failure Program, Heart Institute, Children’s Health of Orange County, Orange, CA, USA; Chapman University, Orange, CA, USA

AL

Alfonso Limon

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.

Affiliations and expertise
Principal, Oneirix Labs, USA

FJ

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, USA

LY

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, USA

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