AI in Clinical Medicine E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 1080

Veröffentlichungsjahr: 2023

Beschreibung

AI IN CLINICAL MEDICINE 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.

Leseprobe

Table of Contents

Cover

Title Page

Copyright Page

Dedication

List of Contributors

Editor Biographies

Editorial Team

Alphabetical List of Authors

Foreword

Preface

Why This Book, Why Now?

The Promise of AI in Clinical Medicine

What We Didn’t Cover in This Edition

How This Book Is Organized

Acknowledgements

Cover Acknowledgement

Relevant AI Terms

About the Companion Website

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

1 An Introduction to AI for Non‐Experts

1.1 Introduction

1.2 Machine Learning

1.3 Strategies to Train Algorithms

1.4 Underfitting and Overfitting

1.5 Data Preparation and Its Importance

1.6 Artificial Neural Network

1.7 Training a Neural Network: All You Need to Know

1.8 Advanced Techniques

2 General Framework for Using AI in Clinical Practice

2.1 Introduction

2.2 AI in Clinical Use Framework

2.3 Patient Physician Trust

2.4 Regulation

2.5 Evidence and Bias

2.6 Automation

2.7 Ethics and Liability

2.8 Reimbursement

2.9 Equity

References

3 AI and Medical Education

3.1 Introduction

3.2 Competency in AI: Preparing for the Era of AI in Medicine

3.3 AI Tools to Enhance Medical Education Itself

3.4 Perspectives and Best Practices in Implementing Changes to the Medical Curriculum

3.5 Future Directions

Disclosure Statement

References

II: AI Foundations

4 History of AI in Clinical Medicine

4.1 Introduction

4.2 The Beginnings of AI in Medicine

4.3 The Development of Artificial Neural Networks

4.4 The Era of Support Vector Machines and Feature Descriptors

4.5 Dominance of Deep Learning

4.6 Interpretability of Deep Models

4.7 Transformers: Better Performance and Inherent Interpretability through Self‐Attention

4.8 The (Near) Future

Funding

References

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

5.1 Introduction

5.2 Core Concepts of AI

5.3 Roles of AI in Relation to Physicians

5.4 Concluding Remarks

References

6 Building Blocks of AI

6.1 Introduction and AI Definitions

6.2 Challenges and Failures in Healthcare AI from a ‘Building Blocks’ Perspective

6.3 Conclusion

References

7 Expert Systems for Interpretable Decisions in the Clinical Domain

7.1 Introduction

7.2 Deep Learning in Clinical Medicine

7.3 Interpretability versus Explainability

7.4 Expert Systems in the Clinical Domain

7.5 Roadmap to Knowledge‐Driven Deep Learning

7.6 Potential Applications of Mixed Intelligence Systems

7.7 Conclusion

References

8 The Role of Natural Language Processing in Intelligence‐Based Medicine

8.1 Introduction

8.2 Introducing a Few Common and Important Natural Language Processing Techniques in Healthcare

8.3 Summary of State‐of‐the‐Art Platforms for Natural Language Processing in Healthcare

8.4 Use Case Project for Natural Language Processing – Approaches and Challenges

8.5 Conclusions

Conflicts of Interest

References

III: AI Applied to Clinical Medicine

Frontline Care Specialties

9 AI in Primary Care, Preventative Medicine, and Triage

9.1 Introduction

9.2 Primary Care

9.3 Preventative Medicine

9.4 Triage

9.5 Challenges

Conflicts of Interest

References

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

10.1 Introduction

10.2 Psychological Health Assessment Tools

10.3 Modalities for Mental Health Assessment

10.4 Machine Learning for Mental Health Diagnosis – Wearable Sensors and AI

10.5 Discussion and Future Roadmap

References

11 AI in Dentistry

11.1 Introduction

11.2 Current Trends: What Is the Focus of Research?

11.3 Use Cases and Application Opportunities

11.4 AI and Dentistry Regulations in the USA

11.5 Challenges

11.6 Roadmap to the Future

Conflicts of Interest

References

12 AI in Emergency Medicine

12.1 Introduction

12.2 Prehospital

12.3 Cardiac Arrest

12.4 Triage

12.5 Monitoring

12.6 Electrocardiogram

12.7 Imaging

12.8 Patient Assessment and Outcome Prediction

12.9 Departmental Management

12.10 Public Health: Opioid Overdose and Disease Outbreaks

12.11 Success in Implementing AI in the Emergency Department

12.12 Challenges

12.13 Future Possibilities

12.14 Chapter Limitations

References

Medical Specialties

13 AI in Respirology and Bronchoscopy

13.1 Introduction

13.2 Brief Overview of AI and Machine Learning

13.3 Innovative Research Using AI in Bronchoscopy

13.4 Current Clinical Applications of AI in Bronchoscopy

13.5 Final Word

References

14 AI in Cardiology and Cardiac Surgery

14.1 Introduction

14.2 Cardiology Imaging and Electrophysiology

14.3 Solutions for Specific Tasks

14.4 Cardiac Surgery

14.5 Remaining Challenges

14.6 Conclusion

References

15 AI in the Intensive Care Unit

15.1 Introduction

15.2 AI Methodology in the ICU

15.3 Clinical Scoring Systems

15.4 Improving Sepsis Recognition with AI

15.5 Reinforcement Learning in ICU

15.6 Deep Learning in ICU

15.7 Limitations and Future Directions

Conflicts of Interest

References

16 AI in Dermatology

16.1 Introduction

16.2 History of AI in Dermatology

16.3 Potential Clinical Applications

16.4 The Path to Clinical Implementation

References

17 Artificial Intelligence in Gastroenterology

17.1 Introduction

17.2 Colonoscopy

17.3 Esophagogastroduodenoscopy

17.4 Video Capsule Endoscopy

17.5 Endoscopic Ultrasound

17.6 Clinical Prediction Models

17.7 Regulation

17.8 Future Directions

Conflicts of Interest

References

18 AI in Haematology

18.1 Introduction

18.2 Current Thinking, Testing, and Best Practice in Haematology

18.3 The Implementation of AI in Haematology

18.4 Current Applications of AI in Haematology

18.5 Considerations and Challenges for AI in Haematology

18.6 The Future of AI in Haematology

References

19 AI and Infectious Diseases

19.1 Introduction

19.2 Disease Outbreaks and Surveillance

19.3 Disease Diagnosis

19.4 Prediction and Control of Antimicrobial Resistance

19.5 Disease Treatment

19.6 Conclusion

References

20 AI in Precision Medicine: The Way Forward

20.1 Precision Medicine: The Future of Healthcare

20.2 The Way Forward

20.3 Benefits of Precision Medicine in the Clinical Care Pathway

20.4 Investing in Precision Medicine–Based Drugs Is the Future of Drug Development

20.5 Healthcare Stakeholders and Their Role in the Success of Precision Medicine

20.6 AI, Big Data, Wearables, and Real‐World Data

20.7 Future Perspective

References

21 AI in Paediatrics

21.1 Introduction

21.2 The Use of AI in Paediatrics

21.3 Challenges for AI in Paediatric Healthcare

21.4 Future Directions

References

22 AI Applications in Rheumatology

22.1 Introduction

22.2 Inflammation

22.3 Damage

22.4 Disease Activity

22.5 Systemic Sclerosis

22.6 Limitations and Future Perspectives

References

Surgical Specialties

23 Perspectives on AI in Anaesthesiology

23.1 Introduction

23.2 AI in Peri‐operative Patient Risk Stratification

23.3 AI in intra‐operative Management

23.4 AI in Post‐operative Management and Discharge Planning

23.5 Imaging and Technical Skills Aid

23.6 Conclusion

Conflicts of Interest

References

24 AI in Ear, Nose, and Throat

24.1 Introduction

24.2 Ear, Nose, and Throat

24.3 Ear

24.4 Nose

24.5 Throat

24.6 The Future of AI in ENT

References

25 AI in Obstetrics and Gynaecology

25.1 Introduction

25.2 Reproductive Medicine

25.3 Early Pregnancy

25.4 Antenatal Care

25.5 Pregnancy Ultrasonography

25.6 Foetal Heart Rate Monitoring

25.7 Intrapartum Care

25.8 Postnatal Care

25.9 Menopause

25.10 Gynaeoncology

25.11 The Future of AI in Obstetrics and Gynaecology

Declarations of Interest

References

26 AI in Ophthalmology

26.1 Introduction

26.2 Looking into the Eye: A Historical Perspective

26.3 Deep Learning and Ophthalmology

26.4 AI‐Informed Diagnostics in the Eye Clinic

26.5 Assistive Technology Applications of AI in Ophthalmology

26.6 Caveats and Challenges

26.7 The Future of Artificial Intelligence in Ophthalmology

References

27 AI in Orthopaedic Surgery

27.1 Activity Tracking and Digital Outcomes

27.2 Image Processing and Analysis

27.3 Clinical Outcome Prediction and Decision Support

27.4 Health Systems Efficiency and Optimization

Conflicts of Interests

References

28 AI in Surgery

28.1 Introduction

28.2 Pre‐operative Care

28.3 Intra‐operative Care

28.4 Postoperative Care and Long‐Term Management

28.5 Surgical Training

28.6 Challenges and Opportunities

28.7 Caveats for AI in Surgery

28.8 Conclusion

References

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

29.1 Introduction

29.2 Deep Learning Algorithms for Prostate Cancer Diagnosis

29.3 Future of AI in Prostate Cancer Diagnosis

References

Diagnostic Specialties

30 AI in Pathology

30.1 Digital Pathology: Beginning of a New Era

30.2 Successful Applications of Clinical Pathology AI: The Basics

30.3 AI for Streamlined Clinical Workflow

30.4 Major Challenges

30.5 Future Potential

30.6 Conclusions

References

31 Introduction to AI in Radiology

31.1 Introduction

31.2 AI in Diagnostic Imaging

31.3 Enhancement of Radiology Workflow

31.4 Medical Imaging Processing – Powered by AI

31.5 Future Outlook and Obstacles

References

32 Clinical Applications of AI in Diagnostic Imaging

32.1 Introduction

32.2 Clinical AI Solutions in Radiology

32.3 Evaluation of AI Tools for Clinical Implementation

32.4 The Hidden Costs of Implementing AI in Practice

32.5 Conclusion

References

33 AI for Workflow Enhancement in Radiology

33.1 Introduction to Imaging Workflow

33.2 Image Display Protocols

33.3 Quality Assurance and Peer Review: ‘Second Reader’ Applications

33.4 Clinical Decision Support

33.5 Natural Language Processing

33.6 Medical Image Protocolling

33.7 Image Acquisition and AI support

33.8 Imaging Pathway in an Emergency and Trauma Radiology Department

33.9 Challenges Related to AI Use

33.10 Conclusion

References

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

34.1 Introduction

34.2 Basic Introduction to Medical Image Processing Concepts

34.3 Basic AI‐Based Image Quality Improvement Approaches

34.4 Specific Problems and AI‐Based Solutions

34.5 Potential Pitfalls

34.6 Looking Ahead: Beyond Image Acquisition

34.7 Conclusions

References

35 Future Developments and Assimilation of AI in Radiology

35.1 Introduction

35.2 Future of Radiology Reporting

35.3 Beyond the Radiologist

35.4 Future for Patients

35.5 Future for Administrative Staff

35.6 Ethical Considerations in Radiology

35.7 Legal and Ethical Challenges in Radiology

35.8 Differential Role of AI across Imaging Modalities

35.9 Using Future Predictions to Guide Current Planning

35.10 Determining Who Pays

35.11 Conclusion

References

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

AI Regulation, Privacy, Law

36 Medical Device AI Regulatory Expectations

36.1 Introduction

36.2 EU Regulatory Requirements for Medical Devices, Including Software and AI

36.3 Health Canada Regulatory Requirements for Medical Devices Including Software

36.4 FDA Regulatory Requirements for Medical Devices Including Software

36.5 FDA’s Action Plans for AI/Machine Learning in SaMD

36.6 Conclusion

References

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

37.1 Introduction

37.2 United States of America

37.3 Europe

37.4 South Africa

37.5 Similarities and Differences between HIPAA, GDPR, and the POPI Act

37.6 Conclusion

Acknowledgments

References

38 AI‐Enabled Consumer‐Facing Health Technology

38.1 Introduction

38.2 Trends and Patient Receptivity to AI‐Enabled Consumer‐Facing Health Technology

38.3 Different Types of AI‐Enabled Consumer‐Facing Health Technology

38.4 Common Issues for Consumer‐Facing AI‐Enabled Health Technology

38.5 Emerging Standards and Regulation Efforts

38.6 Best Practices for Physician Involvement with the Creation and Validation of Consumer‐Facing AI‐Enabled Health Technology

38.7 Best Practices for Recommending and Responding to Patient Queries about AI‐Enabled Consumer‐Facing Health Technology

38.8 Future Directions for AI‐Enabled Consumer‐Facing Health Technology

Conflicts of Interest

References

Ethics, Equity, Bias

39 Biases in Machine Learning in Healthcare

39.1 Introduction

39.2 Pre‐existing Disparities

39.3 Risks in Implementing Machine Learning in Healthcare

39.4 Next Steps and Solutions

References

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

40.1 Introduction

40.2 Business and Use Case Development

40.3 Design Phase

40.4 Data

40.5 Building

40.6 Testing

40.7 Deployment

40.8 Monitoring

40.9 Conclusion

References

Design and Implementation

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

41.1 Introduction

41.2 Background in Human–Computer Interaction

41.3 Designing Human‐AI in Healthcare

41.4 Evaluating Human‐AI in Healthcare

41.5 Open Research Questions

41.6 Conclusion

References

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

42.1 Clinical Evaluation and Study Design for AI in Clinical Medicine

42.2 Complete and Transparent Reporting of AI Studies

42.3 Conclusion

References

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

43.1 Prevalence of Video in Medicine

43.2 The Solution: Thoughtful Video Data Capture

43.3 Options for Building Video Data Capture Infrastructure

43.4 How to Leverage Video Infrastructure for AI Development

43.5 IT, Security, and Legal Considerations When Implementing Video Capture Infrastructure

43.6 Ancillary Benefits of Implementing Video Capture in Clinical Practice

43.7 Conclusion

References

The Way Forward

44 AI and the Evolution of the Patient–Physician Relationship

44.1 Introduction

44.2 Patient–Physician Relationship

44.3 AI in the Clinical Context

44.4 AI Impact Scenarios

44.5 AI Development with Humans

44.6 Conclusion

References

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

45.1 Understanding Digital Transformation

45.2 Vision of Virtual Care and AI Synergy

45.3 Understanding the Virtual Care as a Service (VCaaS) Model

45.4 Understanding the AI as a Service (AIaaS) Model

45.5 Convergence and Synergy between VCaaS and AIaaS and Other Technologies

45.6 Evolution of Legal and Regulatory Frameworks

45.7 FDA Encourages the Use of AI in Virtual Care

45.8 Value and Reimbursement

45.9 Evolution of Stroke Care in the Digital Era

45.10 Full‐Stack Digital Clinician

45.11 Conclusion

References

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

46.1 Introduction

46.2 Foundations of AI and Machine Learning

46.3 Types of Learning

46.4 AI Model Review

46.5 Data Quality and Data Standards

46.6 Measures of AI Model Performance

46.7 Validation of AI Systems

46.8 Review of AI Applications in Clinical Medicine

46.9 Future Directions

Conflicts of Interest

References

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

47.1 Introduction to the Metaverse

47.2 Digital Humans and Genomic Information

47.3 Immortality in the Metaverse

47.4 AI and VR in Drug Discovery

47.5 Immersive Environments in Life Sciences

47.6 Digital Twins

47.7 Gamification to Connect and Bring Healthcare Providers and Consumers Together

47.8 Telemedicine

47.9 Facilitating Collaboration Among Healthcare Professionals

47.10 Conclusions

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1: Navigating Through The Key Steps of Machine Learning

Table 1.2: Neural Network Components

Table 1.3: Advanced Techniques In Machine Learning

Chapter 2

Table 2.1: International Medical Device Regulators Forum Software as a Medi...

Chapter 6

Table 6.1: Some of the common components of deep learning architectures. AI...

Table 6.2: Commonly Used Problem Types in Medical Imaging with Deep Learnin...

Chapter 10

Table 10.1: Recent work in mental health diagnosis using machine learning a...

Chapter 27

Table 27.1: Mirels score for predicting risk of pathological fracture [5]. ...

Chapter 32

Table 32.1: Definition of Terminology Used to Describe Commercial Ai Soluti...

Chapter 33

Table 33.1: Applications of AI for radiology workflow enhancement.

Chapter 36

Table 36.1: MDCG 2019‐11 Qualification and Classification of Software. This...

Table 36.2: Health Canada’s Canadian Medical Device Regulations (cMDR). Thi...

Table 36.3: Documentation requirements comparison between 2005 and 2021 gui...

Table 36.4: Summary of changes in guidance requirements.

Table 36.5: Comparison of documentation between Guidance and IEC 62304.

Chapter 42

Table 42.1: Summary of Published Clinical Trials Evaluating Ai Systems.

Chapter 43

Table 43.1: Summary of Capabilities for Different Video Capture Options.

List of Illustrations

Chapter 1

Figure 1.1: One layered neural network, and an artificial neural network. (a...

Figure 1.2: Machine learning strategies. Circles represent data samples whil...

Figure 1.3: Illustration of performance judgement that affects model general...

Figure 1.4: Training, validation and testing phases in machine learning mode...

Figure 1.5: Error (also known as loss) minimization during training iteratio...

Chapter 2

Figure 2.1: AI in clinical use framework.

Figure 2.2: Levels of AI automation.

Chapter 3

Figure 3.1: Revised Bloom Taxonomy adapted as a model for the continuum for ...

Chapter 4

Figure 4.1: Traditional AI (top) versus explainable AI (XAI, bottom). Tradit...

Chapter 5

Figure 5.1: (a–d) Timeline of AI in clinical medicine from 1950 to the prese...

Chapter 6

Figure 6.1: Venn diagram of AI nomenclature.

Figure 6.2: A typical framework illustrating deep learning design principles...

Figure 6.3: Illustration of a convolutional neural network (CNN). (a) How CN...

Chapter 7

Figure 7.1: A workflow for mixed intelligence systems using a knowledge base...

Chapter 8

Figure 8.1: Examples of pathology reports with extracted labels. The terms l...

Figure 8.2: Accuracy performance for Azure and Google AutoML for all labels ...

Chapter 9

Figure 9.1: Schematic demonstrating how NLP is enabling information within t...

Figure 9.2: A decision support system (DSS) framework to provide patient‐spe...

Chapter 10

Figure 10.1: Sample electroencephalogram (EEG) signals. (a) Raw EEG data acq...

Figure 10.2: Roadmap for an efficient AI‐driven real‐time mental health self...

Chapter 11

Figure 11.1: (a) Examples of caries decay detected on various tooth surfaces...

Figure 11.2: (a) An example of automatically detected tooth, filling, and bo...

Figure 11.3: (a) A patient's conditions before treatment as automatically se...

Figure 11.4: An example of dental charting workflow automation: patients' im...

Figure 11.5: An example of automated processing of the sequence of commands:...

Figure 11.6: Crown automatically generated using generative adversarial netw...

Figure 11.7: An example of automatically segmented dental structures for con...

Figure 11.8: An example of systematic disagreement in establishing the refer...

Chapter 13

Figure 13.1: Vocal cords (green bounding box) and tracheal rings (blue bound...

Figure 13.2: Structure of CaffeNet showing radial probe endobronchial ultras...

Figure 13.3: Automatic representative images selection model for strain elas...

Figure 13.4: Automated classification of lung cancer types from cytological ...

Figure 13.5: Genomic sequencing classifier structure showing (a) overall str...

Figure 13.6: Optimized deep‐airway segmentation. (a) A thoracic computed tom...

Figure 13.7: MONARCH

®

Platform.

Figure 13.8: Ion™. ©2021 Intuitive Surgical, Inc.

Figure 13.9: LungVision™. ©2021 Body Vision Medical.

Chapter 14

Figure 14.1: Haemodynamic biomarkers automatically estimated by the RuiXin‐F...

Chapter 15

Figure 15.1: The Vitaliti™ continuous vital sign monitor and user interface....

Figure 15.2: (a) The Intelligent ICU system includes wearable accelerometer ...

Figure 15.3: Overview of transparent, trust‐centric design methodology for C...

Chapter 16

Figure 16.1: Potential clinical deployments of AI in dermatology.

Figure 16.2: Examples of images in which an AI model agrees or disagrees wit...

Chapter 17

Figure 17.1: Example output from a computer‐aided detection system using whi...

Figure 17.2: Example output from a computer‐aided diagnosis system. The syst...

Figure 17.3: Real‐time continuous AI algorithm detection of Barrett's dyspla...

Chapter 18

Figure 18.1: Hierarchical blood cell organization. At the core of a normal h...

Chapter 19

Figure 19.1: Model development workflow. This AI‐based detection system perm...

Figure 19.2: Grad‐CAM for three chest X‐ray images of patients with the diag...

Chapter 20

Figure 20.1: Building a knowledge network and taxonomical classifier to pred...

Figure 20.2: Gene variant analysis in the context of each patient's disease ...

Figure 20.3: The future of medicine is precise. We are moving from the past,...

Chapter 21

Figure 21.1: Results of a PubMed search, September 2021.

Figure 21.2: The range of patients in the paediatric intensive care unit (a)...

Figure 21.3: Alder Hey Innovation – cognitive hospital concept.

Figure 21.4: (a, b) Alder Hey Innovation – AI digital assistant.

Chapter 22

Figure 22.1: (a, b) Sonographic tenosynovitis in an inflammatory arthritis p...

Figure 22.2: Superficial temporal artery ultrasound scan with a positive 'ha...

Figure 22.3: Axial magnetic resonance imaging of wrist (a) processed by a co...

Figure 22.4: Dorsal longitudinal (a) and transverse (b) scans of the hyaline...

Figure 22.5: Axial post‐gadolinium fat‐saturated T1‐weighted magnetic resona...

Figure 22.6: (a–c) Classification into arteries and veins using deep learnin...

Chapter 23

Figure 23.1: Opportunities for integration of AI technologies in anaesthesio...

Chapter 24

Figure 24.1: Assistive diagnostics performed by AI models created by TympaHe...

Chapter 26

Figure 26.1: Two pictures from ophthalmic imaging history. (a) Photographing...

Figure 26.2: Example of a convolution in which a filter convolves an image t...

Figure 26.3: Example of the segmentation of morphological features used in t...

Figure 26.4: A visualization screenshot from the Moorfields‐DeepMind deep le...

Figure 26.5: An example deep learning model for automatic segmentation of ge...

Figure 26.6: An example of a deep learning system for diabetic retinopathy (...

Figure 26.7: AI‐assisted smart glasses can help the visually impaired using ...

Chapter 27

Figure 27.1: Classification of shoulder physical therapy exercise by a patie...

Figure 27.2: Example of a convolutional recurrent neural network architectur...

Figure 27.3: AI image analysis. Examples of tasks that are commonly performe...

Figure 27.4: Average daily demand on the orthopaedic service stratified by m...

Figure 27.5: Prediction of orthopaedic trauma volumes at a level I trauma ce...

Chapter 28

Figure 28.1: The operating room of the future. Various AI technologies are s...

Figure 28.2: A snapshot of the interactive AI‐based POTTER risk calculator. ...

Figure 28.3: GoNoGoNet model prediction showing safe (green overlay) and dan...

Chapter 29

Figure 29.1: Imaging to identify areas of increased prostate cancer risk in ...

Chapter 30

Figure 30.1: Whole slide imaging of bladder cancer specimen from TCGA‐BLCA c...

Figure 30.2: Example tumour‐infiltrating lymphocyte (TIL) detection map for ...

Chapter 32

Figure 32.1: Product description page and data sheet curated by the editors ...

Figure 32.2: Automated alert and image postprocessing of a suspected large v...

Figure 32.3: LCP score as seen in Optellum’s Virtual Nodule Clinic. A score ...

Figure 32.4: Examples of computer‐aided detection and diagnosis (CADe/x) sol...

Figure 32.5: Example of GE Healthcare’s automated prostate workflow PROView ...

Figure 32.6: Automated computed tomography (CT)‐based cardiometabolic tools ...

Chapter 33

Figure 33.1: Proposed AI‐guided clinical decision‐support model. In this exa...

Figure 33.2: AI supported workflow in an emergency and trauma radiology depa...

Chapter 34

Figure 34.1: Image formation in radiography. An X‐ray source is used to shin...

Figure 34.2: Basic operation of a computed tomography scanner. The patient (...

Figure 34.3: Basic principles of magnetic resonance imaging application. Som...

Figure 34.4: Sensor and image domain–based solutions. (a) In sensor domain–b...

Chapter 35

Figure 35.1: Timeline of progress in radiomics. The evolution of radiomics f...

Figure 35.2: Challenges in data protection. With increasing cyber‐crimes, th...

Figure 35.3: Radiology AI ecosystem. The radiology ecosystem is complex, and...

Figure 35.4: Sources of liability. The pyramid of liability is multi‐pronged...

Chapter 36

Figure 36.1: Assigning software safety classification.

Chapter 38

Figure 38.1: Trends fuelling the rise of AI‐enabled consumer‐facing health t...

Figure 38.2: Broad categories for AI‐enabled consumer‐facing health technolo...

Chapter 40

Figure 40.1: The AI lifecycle. UK Information Commissioner’s Office algorith...

Chapter 43

Figure 43.1: Example of a web portal for accessing endoscopy videos in cloud...

Chapter 45

Figure 45.1: Digital transformation of healthcare in the context of the virt...

Figure 45.2: Virtual Hospital at Home. This is the ultimate expression of th...

Figure 45.3: AI as a Service (AIaaS) in healthcare can be provided as a modu...

Chapter 46

Figure 46.1: Linear relationships between words denoting male and female rel...

Figure 46.2: Schematic depiction of supervised learning set up as a predicti...

Figure 46.3: Schematic depiction of cross‐validation. A set of labelled data...

Figure 46.4: Three types of distance measures between points

a

and

b

. Euclid...

Figure 46.5: A schematic of a collaborative filter recommender system.

Figure 46.6: A confusion matrix with several classification metrics listed....

Figure 46.7: Understanding an ROC diagram for a binary classifier.

Figure 46.8: Relative comparison of different tasks in which AI solutions ha...

Guide

Cover Page

Title Page

Copyright Page

Dedication

List of Contributors

Foreword

Preface

Acknowledgements

Relevant AI Terms

About the Companion Website

Table of Contents

Begin Reading

Index

WILEY END USER LICENSE AGREEMENT

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AI in Clinical Medicine

A Practical Guide for Healthcare Professionals

Lead Editor

Michael F. Byrne

Co‐editors

Nasim Parsa, Alexandra T. Greenhill, Daljeet Chahal, Omer Ahmad, Ulas Bagci

Editorial Manager

Chrystal Palatý

This edition first published 2023© 2023 John Wiley & Sons Ltd

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

The right of Michael F. Byrne, Nasim Parsa, Alexandra T. Greenhill, Daljeet Chahal, Omer Ahmad, and Ulas Bagci to be identified as the editorial material in this work has been asserted in accordance with law.

Registered OfficesJohn Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USAJohn Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK

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Limit of Liability/Disclaimer of WarrantyThe contents of this work are intended to further general scientific research, understanding, and discussion only and are not intended and should not be relied upon as recommending or promoting scientific method, diagnosis, or treatment by physicians for any particular patient. In view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of medicines, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each medicine, equipment, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

Library of Congress Cataloging‐in‐Publication DataNames: Byrne, Michael F., editor.Title: AI in clinical medicine : a practical guide for healthcare professionals / lead editor, Michael F. Byrne ; co-editors, Nasim Parsa, Alexandra T. Greenhill, Daljeet Chahal, Omer Ahmad, and Ulas Bagci.Description: Hoboken, NJ : Wiley-Blackwell, 2023. | Includes bibliographical references and index.Identifiers: LCCN 2022045648 | ISBN 9781119790648 (hardback) | ISBN 978111979066248 (ePDF) | ISBN 9781119790679 (epub) | ISBN 9781119790686 (oBook)Subjects: MESH: Artificial Intelligence | Clinical Medicine | Medical InformaticsClassification: LCC R859.7.A78 | NLM W 26.55.A7 | DDC 610.285/63–dc23/eng/20221209LC record available at https://lccn.loc.gov/2022045648

Cover Design: WileyCover Image: used with the permission of Aidan Meller of the Ai‐Da Robot project – www.ai‐darobot.com

Dedication

I dedicate this book to my beloved parents, Tom and Philomena, who truly nurtured and encouraged me, and genuinely made countless sacrifices to give me the wonderful opportunities in life that I have; to my brother and best friend, Sean, always there for support and laughs; and, of course, to my truly wonderful, patient, supportive, loving, and beautiful wife, Vivian. I hope this book goes some small way to show my gratitude to all of them.

—Michael F. Byrne, Lead Editor

List of Contributors

Editor Biographies

Lead Editor, Dr Michael Byrne

Dr Michael Byrne is a Clinical Professor of Medicine in the Division of Gastroenterology, and Director of the Interventional Endoscopy Fellowship programme at Vancouver General Hospital/University of British Columbia.

He is also CEO of Satisfai Health (and founder of ai4gi), a company aiming to deliver precision endoscopy to gastroenterology using artificial intelligence and allied technologies.

Dr Byrne is a graduate of both Cambridge and Liverpool Universities, and trained in advanced endoscopy at Duke University. He holds a doctorate degree from Cambridge University for his molecular science bench research work on Helicobacter pylori and Cyclooxygenase.

He is widely regarded as one of the leading physician experts in artificial intelligence as applied to gastroenterology. He is in huge demand as a medical AI reviewer for all the top medical journals, and is frequently described as one of the pioneers in bringing AI to gastroenterology and endoscopy.

He has presented at many international conferences, particularly in relation to artificial intelligence and gastroenterology.

He has published over 150 papers in peer‐reviewed journals, and over 200 abstracts.

Dr Byrne is a clinical innovator and physician entrepreneur with more than 25 years’ experience as a practising physician.

Editorial Team

Dr Nasim Parsa

Dr Nasim Parsa is a board‐certified gastroenterologist and clinical researcher. She attended Tehran University of Medical Sciences, completed her gastroenterology fellowships at the University of Missouri, and pursued an additional year of training in advanced oesophageal disorders at the Mayo Clinic. She is also the Vice President of Medical Affairs at Satisfai Health Inc., a leading medical solution provider specializing in AI applications in gastroenterology.

Through her work as a clinician and researcher, she has published extensively, with over 30 peer‐reviewed publications and over 40 abstract presentations at national and international meetings. She has been an invited reviewer for several prestigious journals, including Gastroenterology and Gastrointestinal Endoscopy. She is committed to serving her profession, and is currently serving on several GI Society committees, including the Educational Affair Committee for the American College of Gastroenterology (ACG), the Education Committee of the International Society for the Disease of the Esophagus, and the Quality Leadership Council of the American Gastroenterological Association (AGA).

Dr Parsa is passionate about improving patient outcomes through cutting‐edge technology and the meaningful implementation of AI in clinical practice. She is the youngest member of the AI in Clinical Medicine editorial team, and has made significant editorial contributions to this book.

Dr Alexandra T. Greenhill

Dr Alexandra T. Greenhill is one of the leading physicians in health innovation, and the CEO/Chief Medical Officer of Careteam Technologies. After a more than 15‐year career in director and C‐level leadership roles, she has spent the last few years leading and advising some of the most innovative healthtech companies. She is a TEDx and keynote speaker, has been recognized as one of the Top 40 under 40, Most Influential Women in STEM, and WXN Most Powerful Woman, and has received the Queen Elizabeth II Medal of Service.

Dr Daljeet Chahal

Dr Daljeet Chahal completed his bachelor’s degree at the University of Northern British Columbia, followed by his master’s and medical degrees through the University of British Columbia. He completed his internal medicine and gastroenterology training in Vancouver. Dr Chahal is currently completing advanced hepatology training at the Mount Sinai Hospital in New York. He has an interest in applying machine learning technologies to various aspects of clinical medicine, and hopes to incorporate such technologies into his future clinical practice and research endeavors.

Dr Omer Ahmad

Dr Omer Ahmad is a gastroenterologist and senior clinical research scientist at University College London, with a specialist interest in interventional endoscopy, advanced imaging techniques, and computer vision.

His academic work focused on the clinical translation of artificial intelligence in endoscopy, providing experience across the entire translational pipeline for AI software as a medical device. His pioneering interdisciplinary research at UCL led to the development of AI software for real‐time use during colonoscopy, which is currently being used in clinical practice. He was awarded the young clinical and translational scientist of the year by the British Society of Gastroenterology.His specific research interests include identifying barriers to the implementation of artificial intelligence in healthcare. He has published numerous international initiatives related to the effective validation and implementation of AI solutions. He also serves as an expert member on AI working groups for international endoscopy societies and is developing educational programmes to improve foundational knowledge of AI for clinicians.

Dr Ulas Bagci

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, Department of Computer Science, at the University of Central Florida. His research interests include artificial intelligence, machine learning, and their applications in biomedical and clinical imaging. Dr Bagci has more than 250 peer‐reviewed articles in these areas. Previously, he was a staff scientist and lab co‐manager at the National Institutes of Health’s Radiology and Imaging Sciences Department, and Center for Infectious Diseases Imaging. Dr Bagci holds two NIH R01 grants (as Principal Investigator), and serves as a Steering Committee member of AIR (artificial intelligence resource) at the NIH. He has also served as an area chair for MICCAI for several years, and is an Associate Editor of top‐tier journals in his fields such as IEEE Transactions on Medical Imaging, Medical Physics, and Medical Image Analysis. Dr Bagci teaches machine learning, advanced deep learning methods, computer and robot vision, and medical imaging courses. He has several international and national recognitions, including best paper and reviewer awards.

Editorial Manager – Dr Chrystal Palatý

Dr Chrystal Palatý has provided project management and medical writing services to her clients in the medical and health research sectors since founding her company, Metaphase Health Research Consulting Inc. (www.metaphase‐consulting.com), in 2007. She earned her PhD in Experimental Medicine from the University of British Columbia, then continued her research with a postdoctoral position in cell cycle control at the University of Toronto, and molecular mechanisms of DNA repair at Toronto’s Hospital for Sick Children Research Institute.

AI in Clinical Medicine: A Practical Guide for Healthcare Professionals was her most challenging project to date and also one of the most rewarding. She especially enjoyed working with all the editors and authors.

Alphabetical List of Authors

Aazad Abbas, MD(c)Temerty Faculty of Medicine, University of Toronto, Toronto, Canada

Yasmin Abedin, BSc, MBBS, MScComputer Science, University College London, London, UK

Aakanksha Agarwal, MBBS, MD, DNBFellow in Musculoskeletal Radiology, Department of Radiology, University of British Columbia, Vancouver, BC, Canada

Omer F. Ahmad, BSc, MBBS, MRCPWellcome EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, UK

Sharib Ali, MSc, PhDLecturer, School of Computing, Faculty of Engineering and Physical Sciences, University of Leeds, Leeds, UK

Syed Muhammad Anwar, PhDUniversity of Engineering and Technology, Taxila, PakistanSheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC, USA

Charles E. Aunger, MSc, FBCSManaging Director, Health2047, Menlo Park, CA, USA

Ulas BagciMachine and Hybrid Intelligence Lab, Department of Radiology, Department of Biomedical Engineering, Department of Electrical and Computer Engineering, Northwestern University, Chicago, IL, USA

Junaid Bajwa, BSc, MBBS, MRCGP, MRCS, FRCP, MSc, MBAChief Medical Scientist, Microsoft Research (Global)Physician, NHS, UK

Judy L. BarkalHealth2047, Menlo Park, CA, USA

Tyler M. Berzin, MD, MS, FASGECenter for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA

Ramin E. Beygui, MD, MSc, FACSProfessor of Surgery, UCSF School of Medicine, University of California, San Francisco, CA, USAMedical Director, Washington Hospital Healthcare System, Fremont, CA, USA

Abhishek Bhattarcharya, BA, BSSchool of Medicine, University of Michigan, MI, USA

David Burns MD, PhDDivision of Orthopaedic Surgery, Department of Surgery, University of Toronto, Toronto, Canada

Michael F. Byrne, BA, MA, MD (Cantab), MB, ChBClinical Professor of Medicine, Division of Gastroenterology, Vancouver General Hospital, The University of British Columbia, Vancouver, CanadaCEO and Founder, Satisfai Health, Vancouver, Canada

Leo Anthony CeliInstitute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USADepartment of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USADepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

Dipayan Chaudhuri, MD, FRCPCDivision of Critical Care, Department of Medicine, McMaster University, Hamilton, ON, CanadaDepartment of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON, Canada

Jennifer Y. Chen, BADepartment of Dermatology, University of California, San Francisco, CA, USADermatology Service, San Francisco VA Health Care System, San Francisco, CA, USA

Nolan Chen, BScComputer Science and Engineering, Biology, University of California, Berkeley, CA, USA

Leonid L. Chepelev, MD, PhD, FRCPCAssistant Professor, University of Toronto, ON, Canada

Lawrence K. Cohen, PhDHealth2047, Menlo Park, CA, USA

Henri Colt, MD, FCCP, FAWMEmeritus Professor, University of California, Irvine, CA USACertified Affiliate, APPA

Kevin Deasy, BSc, MB, BCh, MRCPIClinical Associate, Respiratory Medicine, Cork University Hospital, Cork, Ireland

Ugur DemirMachine and Hybrid Intelligence Lab, Department of Radiology and Department of Electrical and Computer Engineering, Northwestern University, Chicago, IL, USA

Alastair K. DennistonCollege of Medical and Dental Sciences, University of Birmingham, Birmingham, UKCentre for Regulatory Science and Innovation, Birmingham Health Partners, University of Birmingham, Birmingham, UKNational Institute of Health Research Biomedical Research Centre for Ophthalmology, Moorfields Hospital London NHS Foundation Trust and University College London, Institute of Ophthalmology, London, UKUniversity Hospitals Birmingham NHS Foundation Trust, Birmingham, UK

Lakshmi DeshpandeDesign Lead at XR Labs, Tata Consultancy Services, Mumbai, India

Girish Dwivedi, MD, PhD, FCSANZ, FRACPDepartment of Cardiology, Fiona Stanley Hospital, Murdoch, WA, AustraliaHarry Perkins Institute of Medical Research, Murdoch, WA, AustraliaMedical School, University of Western Australia, Crawley, WA, Australia

Alanna EbigboInternal Medicine III, Department of Gastroenterology and Infectious Diseases, University Hospital Augsburg, Augsburg, Germany

Jesse M. Ehrenfeld, MD, MPH, FAMIA, FASAMedical College of Wisconsin, Milwaukee, WI, USA

Rehman Faryal, MBBSDepartment of Haematology, Mater Misericordiae University Hospital, Dublin, Ireland

Darren Gates, MBChB(Hons), MRCPCH, DTM&HPaediatric Intensive Care Consultant and AI Clinical Lead, Alder Hey Children’s Hospital, Liverpool, UK

Sara GerkeAssistant Professor of Law, Penn State Dickinson Law, Carlisle, PA, USA

Nima John Ghadiri, MA, MB, BChir, MClinEd, MRCP, FHEAConsultant Medical Ophthalmologist, University of Liverpool, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK

Adrian Goudie, MBBS, FACEMEmergency Department, Fiona Stanley Hospital, Murdoch, Australia

Alexandra T. Greenhill, MD, CCFPAssociate Clinical Professor, Department of Family Medicine, University of British Columbia, Vancouver, CanadaCEO and Chief Medical Officer, Careteam Technologies, Vancouver, Canada

Lin Gu, PhDResearcher Scientist, RIKEN AIP, Tokyo, JapanSpecial Researcher, University of Tokyo, Tokyo, Japan

Dexter Hadley, MD, PhDFounding Chief, Division of Artificial IntelligenceAssistant Professor of PathologyDepartments of Clinical and Computer Sciences, University of Central Florida, College of Medicine, Orlando, FL, USA

Diana HanCollege of Medical and Dental Sciences, University of Birmingham, Birmingham, UKCentre for Regulatory Science and Innovation, Birmingham Health Partners, University of Birmingham, Birmingham, UK

Michael Hardisty, PhDDivision of Orthopaedic Surgery, Department of Surgery, University of Toronto, Toronto, CanadaHolland Bone and Joint Program, Sunnybrook Research Institute, Toronto, Canada

Stephanie Harmon, PhDStaff Scientist, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

Iain Hennessey, MBChB(Hons), BSc(Hons), MMIS, FRCS(Paed Surg), FRSAConsultant Paediatric Surgeon and Clinical Director of Innovation, Alder Hey Children’s Hospital, Liverpool, UK

Dora HuangDepartment of Gastroenterology & Hepatology, Beth Israel Deaconess Medical Center, Boston, MA, USA

Ismail IrmakciMachine and Hybrid Intelligence Lab, Department of Radiology, Northwestern University, Chicago, IL, USA

Sabeena Jalal, MBBS, MSc, Msc, Research Fellow, Vancouver General Hospital, Vancouver, BC, Canada

Vesna Janic, BScSatisfai Health, Vancouver, BC, Canada

Junaid Kalia MD, BCMASVice President, VeeMed Inc, Roseville, CA, USAFounder, AINeuroCare.com, Prosper, TX, USANeurocritical Care, Stroke and Epilepsy Specialist

Isaak KavasidisDepartment of Electrical, Electronic and Computer Engineering, University of Catania, Catania, Italy

Pearse A. KeaneNational Institute of Health Research Biomedical Research Centre for Ophthalmology, Moorfields Hospital London NHS Foundation Trust and University College London, Institute of Ophthalmology, London, UK

Elif KelesMachine and Hybrid Intelligence Lab, Department of Radiology, Northwestern University, Chicago, IL, USA

Marcus Kennedy, MD FRCPI FCCPInterventional Pulmonologist, Cork University Hospital, Cork, Ireland

Barry Kevane, MB, PhD, MRCPI, FRCPathConwaySPHERE, Conway Institute, University College Dublin, Dublin, IrelandDepartment of Haematology, Mater Misericordiae University Hospital, Dublin, IrelandSchool of Medicine, University College Dublin, Dublin, Ireland

Taimoor Khan, BEngStarFish Medical, Toronto, ON, Canada

Colm Kirby, MB, BCh BAO, MRCPIDepartment of Rheumatology, Tallaght University Hospital, Dublin, Ireland

Sandeep S. Kohli, MD, FRCPCDivision of Critical Care, Department of Medicine, Oakville Trafalgar Memorial Hospital, Oakville, ON, CanadaAssistant Clinical Professor (adjunct), Department of Medicine, McMaster University, Hamilton, ON, Canada

Vesela Kovacheva, MD, PHDAttending Anesthesiologist and Director of Translational and Clinical Research, Division of Obstetric Anesthesia, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Boston, MA, USAAssistant Professor of Anesthesia, Harvard Medical School, Boston, MA, USA

Xiaoxuan LiuCollege of Medical and Dental Sciences, University of Birmingham, Birmingham, UKCentre for Regulatory Science and Innovation, Birmingham Health Partners, University of Birmingham, Birmingham, UKUniversity Hospitals Birmingham NHS Foundation Trust, Birmingham, UK

Juan Lu, MPEHarry Perkins Institute of Medical Research, Murdoch, WA, AustraliaMedical School, University of Western Australia, Crawley, WA, Australia

Kevin Ma, PhDPostdoctoral Fellow, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

Brian Mac Namee, PhD, BA (Mod)School of Computer Science, University College Dublin, Dublin, Ireland

Lucy MackillopConsultant Obstetric Physician, Oxford University Hospitals NHS Foundation Trust, Oxford, UKChief Medical Officer – Data and Research, EMIS Group plc, Leeds, UKHonorary Senior Clinical Lecturer, Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, UK

Patricia B. Maguire, PhD, BScConwaySPHERE Research Group, Conway Institute, University College Dublin, Dublin, IrelandSchool of Biomolecular & Biomedical Science, University College Dublin, Dublin, IrelandUCD Institute for Discovery, University College Dublin, Dublin, IrelandAI Healthcare Hub, Institute for Discovery, University College Dublin, Dublin, Ireland

Amarpreet Mahil, BScDepartment of Radiology, Vancouver General Hospital, Vancouver, BC, Canada

Sam MathewlynnSenior Registrar in Obstetrics and Gynaecology, Oxford University Hospitals NHS Foundation Trust, Oxford, UKDigital Fellow, National Centre for Maternity Improvement, London, UK

Sherif Mehralivand, MDMolecular Imaging Branch, National Institutes of Health, Bethesda, MD, USA

Helmut MessmannInternal Medicine III, Department of Gastroenterology and Infectious Diseases, University Hospital Augsburg, Augsburg, Germany

Prasun J. Mishra, PhDPresident and Chair, American Association for Precision Medicine (AAPM), Belmont, CA, USAFounder, Agility Pharmaceuticals and Precision BioPharma Inc., Belmont, CA, USA

Mohammed F. Mohammed, MBBS SB‐RAD CIIP, ER/TraumaAbdominal Imaging and Nonvascular Intervention Radiologist, Department of Radiology, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia

Grainne Murphy, MB, BCh BAO, PhD, MRCPIDepartment of Rheumatology, Cork University Hospital, Cork, Ireland

Lisa Murphy BSc, MBChB, MScSenior Policy Manager, NHS England Centre for Improving Data Collaboration, London, UK

Timothy É. Murray, MB, MCh, MBA, MRCS, FFR, FRCPC, EBIRDiagnostic and Interventional Radiologist, Department of Radiology, St. Paul’s Hospital, Vancouver, BC, CanadaClinical Assistant Professor, Department of Radiology, University of British Columbia, Vancouver, BC, Canada

Fionnuala Ní Áinle, MB, PhD, MRCPI, FRCPathConwaySPHERE, Conway Institute, University College Dublin, Dublin, IrelandSchool of Medicine, University College Dublin, Dublin, IrelandDepartment of Haematology, Mater Misericordiae University Hospital and Rotunda Hospital, Dublin, Ireland

Dr Savvas Nicolaou, MD, FRCPCProfessor, University of British Columbia, Vancouver, BC, CanadaDepartment of Radiology, Vancouver General Hospital, Vancouver, BC, Canada

Zachary O’BrienAustralian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, AustraliaDepartment of Critical Care, University of Melbourne, Melbourne, VIC, Australia

Jesutofunmi A. Omiye, MDStanford University School of Medicine, Stanford, CA, USA

Simone PalazzoDepartment of Electrical, Electronic and Computer Engineering, University of Catania, Catania, Italy

Christoph Palm, Dipl.‐Inform.Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Regensburg, Germany

Maryam Panahiazar, PhD, MScBioinformatics, Computer Science and Engineering, University of California, San Francisco, CA, USA

L. Eric Pulver, DDS, FRCD(C), Dip Oral & Maxillofacial SurgeryChief Dental Officer, Denti.AI Technology Inc., Toronto, ON, CanadaAdjunct clinical faculty, Indiana University Dental School, Indianapolis, IN, USA

Sarah Quidwai, MB, BCh BAO, MRCPIDepartment of Rheumatology, Tallaght University Hospital, Dublin, Ireland

Krishan RamdooTympa Health Technologies Ltd, London, UKDivision of Surgery and Interventional Sciences, University College London, London, UKRoyal Free Hospital, London, UK

Harish RaviPrakashAstraZeneca, Waltham, MA, USA

Jesús Rogel‐SalazarTympa Health Technologies Ltd, London, UKBlackett Laboratory, Department of Physics, Imperial College London, London, UKDepartment of Physics, Astronomy and Mathematics, School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK

Elsie G. Ross, MD, MScDivision of Vascular Surgery, Stanford University School of Medicine, Stanford, CA, USA

Gagandeep SachdevaCollege of Medical and Dental Sciences, University of Birmingham, Birmingham, UK

Federica Proietto SalanitriDepartment of Electrical, Electronic and Computer Engineering, University of Catania, Catania, Italy

Matt SchwartzChief Executive Officer, Virgo Surgical Video Solutions, Inc., Carlsbad, CA, USA

Mark A. Shapiro, MA, MBAChief Operating Officer, xCures, Inc., Oakland, CA, USA

Adnan Sheikh, MD FRCPCProfessor, University of British Columbia, Vancouver, BC, Canada

Helen Simons, MEngStarFish Medical, Victoria, BC, Canada

Concetto SpampinatoDepartment of Electrical, Electronic and Computer Engineering, University of Catania, Catania, Italy

Jonathon Stewart, MBBS, MMed(CritCare)Emergency Department, Fiona Stanley Hospital, Murdoch, WA, AustraliaHarry Perkins Institute of Medical Research, Murdoch, WA, AustraliaMedical School, University of Western Australia, Crawley, WA, Australia

Jack W. Stockert, MD, MBAHealth2047, Menlo Park, CA, USA

Ian StrugChief Customer Officer, Virgo Surgical Video Solutions, Inc., Carlsbad, CA, USA

Siddharthan Surveswaran, PhDDepartment of Life Sciences, CHRIST (Deemed to be University), Bangalore, India

Akshay Swaminathan, BAStanford University School of Medicine, Stanford, CA, USA

Paulina B. Szklanna, PhD, BScConwaySPHERE, Conway Institute, University College Dublin, Dublin, IrelandSchool of Biomolecular & Biomedical Science, University College Dublin, Dublin, IrelandAI Healthcare Hub, Institute for Discovery, University College Dublin, Dublin, Ireland

Marty Tenenbaum, PhD, FAAAIChairman, Cancer Commons, Mountain View, CA, USA

Jay Toor MD, MBADivision of Orthopaedic Surgery, Department of Surgery, University of Toronto, Toronto, Canada

Baris Turkbey, MDMolecular Imaging Branch, National Institutes of Health, Bethesda, Maryland, USA

Dmitry TuzoffFounder and CEO, Denti.AI Technology Inc., Toronto, ON, CanadaPhD researcher, Steklov Institute of Mathematics, St Petersburg, Russia

Lyudmila TuzovaCo‐founder and lead researcher, Denti.AI Technology Inc., Toronto, ON, CanadaMSc student, Georgia Institute of Technology, Atlanta, GA, USA

Niels van Berkel, PhDDepartment of Computer Science, Aalborg University, Aalborg, Denmark

Trent Walradt, MDDepartment of Internal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

Maria L. Wei, MD, PhDProfessor, Department of Dermatology, University of California, San Francisco, CA, USADermatology Service, San Francisco VA Health Care System, San Francisco, CA, USA

Luisa Weiss, PhD, MSc, BScConwaySPHERE, Conway Institute, University College Dublin, Dublin, IrelandSchool of Biomolecular & Biomedical Science, University College Dublin, Dublin, Ireland

Jason Yao, MDMcMaster University, Hamilton, ON, Canada

Albert T. Young, MD, MASDepartment of Dermatology, Henry Ford Hospital, Detroit, MI, USA

Shu Min Yu, BScDepartment of Radiology, Vancouver General Hospital, Vancouver, BC, Canada

Foreword

Ten years ago, the technology world was fascinated with SMAC. The perfect storm of social networks, mobile computing, analytics, and cloud computing seemed to be happening all at once. Facebook would soon go public. The iPad and iPhone had captured the world’s imagination, a wonderful, keyboard‐free device with more power than the supercomputers of the 1990s. Cloud computing had just begun, scoffed at by many, but growing fast. Companies everywhere were beginning to apply analytics at scale, reimagining themselves as digital natives. SMAC. Social, Mobile, Analytics, and Cloud. What could possibly be next?

That summer two papers were released, in seemingly different domains.

Geoffrey Hinton et al. published ‘ImageNet classification with deep convolutional neural networks’, a breakthrough in AI that demonstrated how computers could actually

see

. His software combined the power of video game consoles (graphics processing units or GPUs) with recent advances in analytics and cloud computing, creating the first AI that could correctly identify over 1000 different items from millions of images. Hinton and his peers ignited a Cambrian explosion of computer systems that could perceive.

Jennifer Doudna et al. published ‘A programmable dual‐RNA–guided DNA endonuclease in adaptive bacterial immunity’, which to me demonstrated the human ability to modify the programming of biological cells. Jennifer and her team could not only read the source code of life, their biological apparatus could now

change

it. A universe of possibilities opened up, forever changing genomics, medicine, and biology.

As I write this Foreword today, kids in elementary school are dancing to TikTok videos, where video ‘filters’ change their appearance in hilarious ways. These filters implement a far more sophisticated version of Hinton’s algorithm, now running on mobile phones. The Pinocchio of my youth now appears as realistic long noses in videos of their parents at a family dining table, with kids giggling in delight.

Biology students are regularly reading the DNA of strawberries, using hand‐held sequencers with the same power of the larger machines Doudna had in her lab. Astronauts are sequencing their blood on the space station, a prelude to precision medicine and the world’s best care for adventurers that take an eight‐month trip to Mars on SpaceX rockets.

These powerful technologies amplify who we are as humans, and portend a truly exciting future. We can now build computers that see, hear, taste, and feel, then describe what they perceive in human terms. We’re beginning to understand the source code of life, and from that detect cancers and wellness transitions, far earlier and in less invasive ways than today’s biopsies and mammograms.

That’s both exciting – but also unnerving. Humans must make assumptions about the world to function properly. Our