AI in Clinical Medicine A Practical Guide for Healthcare Professionals

An essential overview of the application of artificial intelligence in clinical medicine

AI in Clinical Medicine: A Practical Guide for Healthcare Professionals is the definitive reference book for the emerging and exciting use of AI throughout clinical medicine. AI in Clinical Medicine: A Practical Guide for Healthcare Professionals is divided into four sections. Section 1 provides readers with the basic vocabulary that they require, a framework for AI, and highlights the importance of robust AI training for physicians. Section 2 reviews foundational ideas and concepts, including the history of AI. Section 3 explores how AI is applied to specific disciplines. Section 4 describes emerging trends, and applications of AI in medicine in the future.

Readers will find that this book:

• Describes where AI is currently being used to change practice, and provides successful cases of AI approaches in specific medical domains.
• Dives into the actual implementation of AI in the healthcare setting, and addresses reimbursement, workforce, and many other practical issues.
• Addresses some of the unique challenges associated with AI in clinical medicine including ethical issues, as well as regulatory and privacy concerns.
• Includes bulleted lists of learning objectives, key insights, clinical vignettes, brief examples of where AI is successfully deployed, and examples of potential problematic uses of AI and possible risks.

From radiology, to pathology, dermatology, endoscopy, robotics, virtual reality, and more, AI in Clinical Medicine: A Practical Guide for Healthcare Professionals explores all recent state-of-the-art developments in the field. It is an essential resource for a general medical audience across all disciplines, from students to clinicians, academics to policy makers.

Dr. Michael Byrne, lead editor, is a Clinical Professor of Medicine at the University of British Columbia, and is also CEO and founder of Satisfai Health, a leading provider of AI solutions in Gastroenterology. He is in great demand as a speaker and thought leader on the international medical AI circuit.

Dr. Nasim Parsa is a gastroenterologist and clinical researcher with interest in patient outcomes and meaningful implementation of AI in practice. She is also the Vice President of Medical Affairs at Satisfai Health.

Dr. Alexandra T. Greenhill is one of the leading physicians in health innovation, founder, advisor and board member of some of the best digital health focused organizations on a mission to accelerate the future of health.

Dr. Daljeet Chahal is a board-certified gastroenterologist and clinical researcher who recently finished advanced hepatology training at the Mount Sinai Hospital in New York City. He has returned to Vancouver to practice medicine at the University of British Columbia, and hopes to incorporate machine learning technologies into his future clinical, research, and business endeavors.

Dr. Omer Ahmad is a gastroenterologist and senior clinical translational research scientist at University College London. He has successfully co-developed AI software that is being used in routine clinical practice, and published international initiatives related to the effective implementation of AI solutions.

Dr. Ula?? Bağc?? is an Associate Professor at Northwestern University's Radiology, ECE, and Biomedical Engineering Departments in Chicago, and Courtesy Professor at the Center for Research in Computer Vision (CRCV), Department of Computer Science, at the University of Central Florida (UCF).

Cover Art: Ai-Da is the world’s first ultra-realistic artist robot. Created in February 2019 by Aidan Meller, she had her first solo show at the University of Oxford, called ‘Unsecured Futures’. She has since travelled and exhibited work internationally, and shown her art at many major museums. She continues to create art that challenges our notions of living in a post-humanist era. More information: www.ai-darobot.com

Dedication

Acknowledgements

Contributors

Relevant AI Terms

Foreword

Preface

Michael F. Byrne

Section 1: Overview of Medical AI: The What, the Why, and the How

1             An Introduction to AI for Non-Experts

Sharib Ali and Michael Byrne

2             General Framework for Using AI in Clinical Practice

Judy L. Barkal, Jack W. Stockert, Jesse M. Ehrenfeld, Charles E. Aunger, and Lawrence K. Cohen

3             AI and Medical Education

Greenhill

Section 2: AI Foundations 

4             History of AI in Clinical Medicine

Isaak Kavasidis, F. Proietto Salanitri, Simone Palazzo, Concetto Spampinato

5             History, Core Concepts, and Role of AI in Clinical Medicine

Christoph Palm

6             Building Blocks of AI

Ulas Bagci, Ismail Irmakci, Ugur Demir, Elif Keles

7             Expert Systems for Interpretable Decisions in the Clinical Domain

Syed Muhammad Anwar

8             The Role of Natural Language Processing in AI-Based Medicine

Maryam Panahiazar, Nolan Chen, Ramin E. Beygui, Dexter Hadley

Section 3: AI Applied to Clinical Medicine

Frontline Care Specialties

9             AI in Primary Care, Preventative Medicine, and Triage

Yasmin Abedin, Omer F. Ahmad, Junaid Bajwa

10           Do It Yourself: Wearable Sensors and AI for Self-Assessment of Mental Health

Harish RaviPrakash and Syed Muhammad Anwar

11           AI in Dentistry

Lyudmila Tuzova, Dmitry Tuzoff, L. Eric Pulver

12           AI in Emergency Medicine

Jonathon Stewart, Adrian Goudie, Juan Lu, Girish Dwivedi

Medical Specialties

13           AI in Bronchoscopy

Kevin Deasy, Henri Colt, Marcus Kennedy

14           AI in Cardiology and Cardiac Surgery

Lin Gu

15           AI in the Intensive Care Unit

Dipayan Chaudhuri, Sandeep S. Kohli

16           AI in Dermatology

Albert T. Young, Jennifer Y. Chen, Abhishek Bhattarcharya, Maria L. Wei

17           AI in Gastroenterology

Trent Walradt, Tyler M. Berzin

18           AI in Haematology

Paulina B. Szklanna, Luisa Weiss, Brian Mac Namee, Rehman Faryal, Barry Kevane, Fionnuala Ní Áinle, Patricia B. Maguire

19           AI in Infectious Diseases

Alanna Ebigbo, Helmut Messmann

20           AI in Precision Medicine: The Way Forward

Prasun Mishra

21           AI in Paediatrics

Darren Gates, Iain Hennessey

22           AI Applications in Rheumatology

Sarah Quidwai, Colm Kirby, Grainne Murphy

Surgical Specialties

23           Perspectives on AI in Anaesthesiology

Vesela Kovacheva

24           AI in Ear, Nose, and Throat

Jesús Rogel-Salazar, Krishan Ramdoo

25           AI in Obstetrics and Gynaecology

Sam Mathewlynn, Lucy Mackillop

26           AI in Ophthalmology

Nima John Ghadiri

27           AI in Orthopaedic Surgery

David Burns, Aazad Abbas, Jay Toor, Michael Hardisty

28           AI in Surgery

Jesutofunmi A. Omiye, Akshay Swaminathan, Elsie G. Ross

29           AI in Urological Oncology: Prostate Cancer Diagnosis with Magnetic Resonance Imaging

Sherif Mehralivand, Baris Turkbey

Diagnostic Specialties

30           AI in Pathology

Stephanie Harmon, Kevin Ma

31           Introduction to AI in Radiology

Shu Min Yu, Amarpreet Mahil

32           Clinical Applications of AI in Diagnostic Imaging

Mohammed F. Mohammed, Shu Min Yu, Amarpreet Mahil, Savvas Nicolaou, Adnan Sheikh

33           AI for Workflow Enhancement in Radiology

Sabeena Jalal, Jason Yao, Shu Min Yu, Amarpreet Mahil, Savvas Nicolaou, Adnan Sheikh

34           AI for Medical Image Processing in Radiology: Improving Quality, Accessibility, and Safety

Leonid L. Chepelev, Shu Min Yu, Amarpreet Mahil, Savvas Nicolaou, Adnan Sheikh

35           Future Developments and Assimilation of AI in Radiology

Aakanksha Agarwal, Timothy É. Murray

Section 4: Policy Issues, Practical Implementation, and Future Perspectives in Medical AI

AI Regulation, Privacy, Law

36           Medical AI Regulatory Expectations

Vesna Janic, Helen Simons, Taimoor Khan

37           Privacy Laws in the USA, Europe, and South Africa

Sara Gerke

38           AI-Enabled Consumer-Facing Health Technology

Alexandra T. Greenhill

Ethics, Equity, Bias

39           Biases in Machine Learning in Healthcare

Dora Huang, Leo Anthony Celi, Zachary O’Brien

40           ‘Designing’ Ethics into AI: Ensuring Equality, Equity, and Accessibility

Lisa Murphy

Design and Implementation

41           Making AI Work: Designing and Evaluating AI Systems in Healthcare

Niels van Berkel

42           Demonstrating Clinical Impact for AI Interventions: Importance of Robust Evaluation and Standardized Reporting

Gagandeep Sachdeva, Diana Han, Pearse A. Keane, Alastair K. Denniston, Xiaoxuan Liu

43           The Importance and Benefits of Implementing Modern Data Infrastructure for Video-Based Medicine

Matt Schwartz, Ian Strug

The Way Forward

44           AI and Evolution of the Patient–Physician Relationship

Judy L. Barkal, Jack W. Stockert, Jesse M. Ehrenfeld, Lawrence K. Cohen

45          Virtual Care and AI: The Whole Is Greater Than the Sum of Its Parts

Junaid Kalia

46          Summing It All Up: Evaluation, Integration, and Future Directions for AI in Clinical Medicine

Mark A. Shapiro, Marty Tenenbaum 

47          A Glimpse into the Future: AI, Digital Humans, and the Metaverse – Opportunities and Challenges for Life Sciences in Immersive Ecologies

Siddharthan Surveswaran, Lakshmi Deshpande

Index

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.