Artificial Intelligence in Healthcare
Academic Press Inc (Verlag)
978-0-12-818438-7 (ISBN)
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
| Erscheinungsdatum | 29.06.2020 |
|---|---|
| Verlagsort | San Diego |
| Sprache | englisch |
| Maße | 152 x 229 mm |
| Gewicht | 610 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Informatik ► Weitere Themen ► Bioinformatik | |
| Studium ► Querschnittsbereiche ► Prävention / Gesundheitsförderung | |
| Technik | |
| ISBN-10 | 0-12-818438-8 / 0128184388 |
| ISBN-13 | 978-0-12-818438-7 / 9780128184387 |
| Zustand | Neuware |
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