AI in Clinical Medicine: A Practical Guide for Healthcare Professionals

List of Contributors xii

Foreword xxiv

Preface xxvi

Michael Byrne

Acknowledgements xxx

Relevant AI Terms xxxii

About the Companion Website xxxvi

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

1 An Introduction to AI for Non-Experts 3
Sharib Ali and Michael Byrne

2 General Framework for Using AI in Clinical Practice 13
Judy L Barkal, Jack W Stockert, Jesse M Ehrenfeld, Charles E Aunger, and Lawrence K Cohen

3 AI and Medical Education 27
Alexandra T Greenhill

SECTION II AI Foundations 39

4 History of AI in Clinical Medicine 41
Isaak Kavasidis, Federica Proietto Salanitri, Simone Palazzo, and Concetto Spampinato

5 History, Core Concepts, and Role of AI in Clinical Medicine 49
Christoph Palm

6 Building Blocks of AI 56
Ulas Bagci, Ismail Irmakci, Ugur Demir, and Elif Keles

7 Expert Systems for Interpretable Decisions in the Clinical Domain 66
Syed Muhammad Anwar

8 The Role of Natural Language Processing in Intelligence-Based Medicine 73
Maryam Panahiazar, Nolan Chen, Ramin E Beygui, and Dexter Hadley

SECTION III AI Applied to Clinical Medicine 81

Frontline Care Specialties

9 AI in Primary Care, Preventative Medicine, and Triage 83
Yasmin Abedin, Omer F Ahmad, and Junaid Bajwa

10 Do It Yourself: Wearable Sensors and AI for Self-Assessment of Mental Health 94
Harish RaviPrakash and Syed Muhammad Anwar

11 AI in Dentistry 104
Lyudmila Tuzova, Dmitry Tuzoff, and L Eric Pulver

12 AI in Emergency Medicine 117
Jonathon Stewart, Adrian Goudie, Juan Lu, and Girish Dwivedi

Medical Specialties

13 AI in Respirology and Bronchoscopy 129
Kevin Deasy, Henri Colt, and Marcus Kennedy

14 AI in Cardiology and Cardiac Surgery 144
Lin Gu

15 AI in the Intensive Care Unit 154
Dipayan Chaudhuri and Sandeep S Kohli

16 AI in Dermatology 165
Albert T Young, Jennifer Y Chen, Abhishek Bhattarcharya, and Maria L Wei

17 Artificial Intelligence in Gastroenterology 176
Trent Walradt and Tyler M Berzin

18 AI in Haematology 184
Paulina B Szklanna, Luisa Weiss, Brian Mac Namee, Rehman Faryal, Barry Kevane, Fionnuala Ní Áinle, and Patricia B Maguire

19 AI and Infectious Diseases 192
Alanna Ebigbo and Helmut Messmann

20 AI in Precision Medicine: The Way Forward 200
Prasun J Mishra

21 AI in Paediatrics 210
Darren Gates and Iain Hennessey

22 AI Applications in Rheumatology 219
Sarah Quidwai, Colm Kirby, and Grainne Murphy

Surgical Specialties

23 Perspectives on AI in Anaesthesiology 228
Vesela Kovacheva

24 AI in Ear, Nose, and Throat 233
Jesús Rogel-Salazar and Krishan Ramdoo

25 AI in Obstetrics and Gynaecology 239
Sam Mathewlynn and Lucy Mackillop

26 AI in Ophthalmology 253
Nima John Ghadiri

27 AI in Orthopaedic Surgery 266
David Burns, Aazad Abbas, Jay Toor, and Michael Hardisty

28 AI in Surgery 282
Jesutofunmi A Omiye, Akshay Swaminathan, and Elsie G Ross

29 AI in Urological Oncology: Prostate Cancer Diagnosis with Magnetic Resonance Imaging 298
Sherif Mehralivand and Baris Turkbey

Diagnostic Specialties

30 AI in Pathology 307
Stephanie Harmon and Kevin Ma

31 Introduction to AI in Radiology 318
Shu Min Yu and Amarpreet Mahil

32 Clinical Applications of AI in Diagnostic Imaging 321
Mohammed F Mohammed, Savvas Nicolaou, and Adnan Sheikh

33 AI for Workflow Enhancement in Radiology 337
Sabeena Jalal, Jason Yao, Savvas Nicolaou, and Adnan Sheikh

34 AI for Medical Image Processing: Improving Quality, Accessibility, and Safety 350
Leonid L Chepelev, Savvas Nicolaou, and Adnan Sheikh

35 Future Developments and Assimilation of AI in Radiology 365
Aakanksha Agarwal and Timothy É Murray

SECTION IV Policy Issues, Practical Implementation, and Future Perspectives in Medical AI 377

AI Regulation, Privacy, Law

36 Medical Device AI Regulatory Expectations 379
Vesna Janic, Helen Simons, and Taimoor Khan

37 Privacy Laws in the USA, Europe, and South Africa 395
Sara Gerke

38 AI-Enabled Consumer-Facing Health Technology 407
Alexandra T Greenhill

Ethics, Equity, Bias

39 Biases in Machine Learning in Healthcare 426
Dora Huang, Leo Anthony Celi, and Zachary O'Brien

40 'Designing' Ethics into AI: Ensuring Equality, Equity, and Accessibility 437
Lisa Murphy

Design and Implementation

41 Making AI Work: Designing and Evaluating AI Systems in Healthcare 448
Niels van Berkel

42 Demonstrating Clinical Impact for AI Interventions: Importance of Robust Evaluation and Standardized Reporting 459
Gagandeep Sachdeva, Diana Han, Pearse A Keane, Alastair K Denniston, and Xiaoxuan Liu

43 The Importance and Benefits of Implementing Modern Data Infrastructure for Video-Based Medicine 469
Matt Schwartz and Ian Strug

The Way Forward

44 AI and the Evolution of the Patient--Physician Relationship 478
Judy L Barkal, Jack W Stockert, Jesse M Ehrenfeld, and Lawrence K Cohen

45 Virtual Care and AI: The Whole Is Greater Than the Sum of Its Parts 488
Junaid Kalia

46 Summing It All Up: Evaluation, Integration, and Future Directions for AI in Clinical Medicine 498
Mark A Shapiro and Marty Tenenbaum

47 A Glimpse into the Future: AI, Digital Humans, and the Metaverse -- Opportunities and Challenges for Life Sciences in Immersive Ecologies 521
Siddharthan Surveswaran and Lakshmi Deshpande

Index 528

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 F.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 healthinnovation, 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. Ulas Bagci 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).