Artificial Intelligence in Healthcare

Adam Bohr, PhD is the CEO and co-founder of Sonohaler, an mhealth and medical device company focused on asthma management using acoustic signals and machine learning tools. He is also a co-founder of Zerion ApS, a pharmaceutical company aspiring to transform the pharmaceutical landscape for formulation of poorly soluble drugs. Previously, he was employed as assistant professor at the Department of Pharmacy, University of Copenhagen where he was doing research on drug implants, nanomedicine and microfluidic technology and teaching pharmaceutical technology subjects. He has published more than 45 peer reviewed academic papers and book chapters and has a PhD in Biomedical Engineering from University College London. He is a health futurist and a healthcare AI proponent with a passion for patient centered healthcare technologies. Kaveh Memarzadeh, PhD is currently a Commercial Field Application Scientist at ChemoMetec, a biotechnology company that innovates in the field of automated cell cytometry. He oversaw research management and communications at Orthopaedic Research UK (ORUK), a UK based medical charity that funds projects into the betterment and improvement of human movement and augmentation. He has published numerous peer-reviewed academic papers and has a PhD in nanotechnology, biomaterials and microbiology. He is also a visiting lecturer at University College London, teaching on a range of topics from the future of prosthetics/bionics to utilization of nanotechnology for antimicrobial bone implants. In his spare time, he reads, paints, builds his own gaming computers and utilizes the power of social media to share his passion for nature with hundreds of thousands of people.

List of contributors xi

About the editors xiii

Biographies xv

Preface xxi

Introduction xxiii

1. Current healthcare, big data, and machine learning 1

Adam Bohr and Kaveh Memarzadeh

1.1 Current healthcare practice 1

1.2 Value-based treatments and healthcare services 5

1.3 Increasing data volumes in healthcare 10

1.4 Analytics of healthcare data (machine learning and deep learning) 16

1.5 Conclusions/summary 21

References 22

2. The rise of artificial intelligence in healthcare applications 25

Adam Bohr and Kaveh Memarzadeh

2.1 The new age of healthcare 25

2.2 Precision medicine 28

2.3 Artificial intelligence and medical visualization 33

2.4 Intelligent personal health records 38

2.5 Robotics and artificial intelligence-powered devices 43

2.6 Ambient assisted living 46

2.7 The artificial intelligence can see you now 50

References 57

3. Drug discovery and molecular modeling using artificial intelligence 61

Henrik Bohr

3.1 Introduction. The scope of artificial intelligence in drug discovery 61

3.2 Various types of machine learning in artificial intelligence 64

3.3 Molecular modeling and databases in artificial intelligence for drug

molecules 70

3.4 Computational mechanics ML methods in molecular modeling 72

3.5 Drug characterization using isopotential surfaces 74

3.6 Drug design for neuroreceptors using artificial neural network techniques 75

3.7 Specific use of deep learning in drug design 78

3.8 Possible future artificial intelligence development in drug design and

development 80

References 81

4. Applications of artificial intelligence in drug delivery and pharmaceutical development 85

Stefano Colombo

4.1 The evolving pharmaceutical field 85

4.2 Drug delivery and nanotechnology 89

4.3 Quality-by-design R&D 92

4.4 Artificial intelligence in drug delivery modeling 95

4.5 Artificial intelligence application in pharmaceutical product R&D 98

4.6 Landscape of AI implementation in the drug delivery industry 109

4.7 Conclusion: the way forward 110

References 111

5. Cancer diagnostics and treatment decisions using artificial intelligence 117

Reza Mirnezami

5.1 Background 117

5.2 Artificial intelligence, machine learning, and deep learning in cancer 119

5.3 Artificial intelligence to determine cancer susceptibility 122

5.4 Artificial intelligence for enhanced cancer diagnosis and staging 125

5.5 Artificial intelligence to predict cancer treatment response 127

5.6 Artificial intelligence to predict cancer recurrence and survival 130

5.7 Artificial intelligence for personalized cancer pharmacotherapy 133

5.8 How will artificial intelligence affect ethical practices and patients? 136

5.9 Concluding remarks 137

References 139

6. Artificial intelligence for medical imaging 143

Khanhvi Tran, Johan Peter Bøtker, Arash Aframian and Kaveh Memarzadeh

6.1 Introduction 143

6.2 Outputs of artificial intelligence in radiology/medical imaging 144

6.3 Using artificial intelligence in radiology and overcoming its hurdles 146

6.4 X-rays and artificial intelligence in medical imaging—case 1 (Zebra medical

vision) 151

6.5 Ultrasound and artificial intelligence in medical imaging—case 2

(Butterfly iQ) 156

6.6 Application of artificial intelligence in medical imaging—case 3 (Arterys) 158

6.7 Perspectives 160

References 161

7. Medical devices and artificial intelligence 163

Arash Aframian, Farhad Iranpour and Justin Cobb

7.1 Introduction 163

7.2 The development of artificial intelligence in medical devices 163

7.3 Limitations of artificial intelligence in medical devices 171

7.4 The future frontiers of artificial intelligence in medical devices 172

References 174

8. Artificial intelligence assisted surgery 179

Elan Witkowski and Thomas Ward

8.1 Introduction 179

8.2 Preoperative 179

8.3 Intraoperative 185

8.4 Postoperative 193

8.5 Conclusion 196

References 197

Further reading 202

9. Remote patient monitoring using artificial intelligence 203

Zineb Jeddi and Adam Bohr

9.1 Introduction to remote patient monitoring 203

9.2 Deploying patient monitoring 205

9.3 The role of artificial intelligence in remote patient monitoring 209

9.4 Diabetes prediction and monitoring using artificial intelligence 219

9.5 Cardiac monitoring using artificial intelligence 221

9.6 Neural applications of artificial intelligence and remote patient

monitoring 224

9.7 Conclusions 229

References 230

10. Security, privacy, and information-sharing aspects of healthcare

artificial intelligence 235

Jakub P. Hlávka

10.1 Introduction to digital security and privacy 235

10.2 Security and privacy concerns in healthcare artificial intelligence 237

10.3 Artificial intelligence’s risks and opportunities for data privacy 245

10.4 Addressing threats to health systems and data in the artificial

intelligence age 253

10.5 Defining optimal responses to security, privacy, and information-sharing

challenges in healthcare artificial intelligence 255

10.6 Conclusions 263

Acknowledgements 264

References 265

11. The impact of artificial intelligence on healthcare insurances 271

Rajeev Dutt

11.1 Overview of the global health insurance industry 271

11.2 Key challenges facing the health insurance industry 272

11.3 The application of artificial intelligence in the health insurance industry 274

11.4 Case studies 280

11.5 Moral, ethical, and regulatory concerns regarding the use of artificial

intelligence 280

11.6 The limitations of artificial intelligence 282

11.7 The future of artificial intelligence in the health insurance industry 289

References 290

12. Ethical and legal challenges of artificial intelligence-driven

healthcare 295

Sara Gerke, Timo Minssen and Glenn Cohen

12.1 Understanding “artificial intelligence” 296

12.2 Trends and strategies 296

12.3 Ethical challenges 300

12.4 Legal challenges 306

12.5 Conclusion 327

Acknowledgements 328

References 329

Concluding remarks 337

Index 339