Adam Bohr holds a PhD in Biomedical Engineering (2013) from University College London and has over a decade of experience in pharmaceutical and healthcare sciences across both academia and industry. He currently works at Novo Nordisk, where he leads AI and digital transformation initiatives in drug discovery and development. Dr. Bohr has a strong background in applying artificial intelligence to healthcare and in addition to his industry role, he is the founder and technical lead of Sonohaler, a company developing AI-driven solutions to support the management of respiratory conditions. His expertise spans the full spectrum of AI applications in healthcare, from early-stage research to real-world deployment. Dr. Bohr is also the co-editor of the book, Artificial Intelligence in Healthcare, which received wide recognition and readership and has a solid track record of peer-reviewed publications. Kaveh Memarzadeh, PhD is currently a Senior Manager at ChemoMetec, a biotechnology company that innovates in the field of automated cell cytometry. He holds research affiliations with University College London and Queen Mary University. He previously 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.

"This book is overall a very complete and organized book that gives newly interested persons a look at AI and its applications. The first few chapters give readers a chance to get used to the vocabulary and give small but effective diagrams explaining often confused concepts such as machine learning versus deep learning. The first few chapters may be extremely dry if the readers are fairly familiar with AI. Therefore, I would recommend this book for readers who feel unsure of their foundation in AI. I would say of the books I have read on digital health in general, this book is more focused. If you compare to Data Pulse: A Brief Tour of Artificial Intelligence in Healthcare, Marcetich (New Degree Press, 2020) or Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, Topal (Basic Books, 2019), both put a focus on challenges of AI, whereas this book has a large focus on the opportunities and covers them in great detail." --Doody Reviews

• List of contributors xiAbout the editors xiiiBiographies xvPreface xxiIntroduction xxiii1. Current healthcare, big data, and machine learning 1Adam Bohr and Kaveh Memarzadeh1.1 Current healthcare practice 11.2 Value-based treatments and healthcare services 51.3 Increasing data volumes in healthcare 101.4 Analytics of healthcare data (machine learning and deep learning) 161.5 Conclusions/summary 21References 222. The rise of artificial intelligence in healthcare applications 25Adam Bohr and Kaveh Memarzadeh2.1 The new age of healthcare 252.2 Precision medicine 282.3 Artificial intelligence and medical visualization 332.4 Intelligent personal health records 382.5 Robotics and artificial intelligence-powered devices 432.6 Ambient assisted living 462.7 The artificial intelligence can see you now 50References 573. Drug discovery and molecular modeling using artificial intelligence 61Henrik Bohr3.1 Introduction. The scope of artificial intelligence in drug discovery 613.2 Various types of machine learning in artificial intelligence 643.3 Molecular modeling and databases in artificial intelligence for drugmolecules 703.4 Computational mechanics ML methods in molecular modeling 723.5 Drug characterization using isopotential surfaces 743.6 Drug design for neuroreceptors using artificial neural network techniques 753.7 Specific use of deep learning in drug design 783.8 Possible future artificial intelligence development in drug design anddevelopment 80References 814. Applications of artificial intelligence in drug delivery and pharmaceutical development 85Stefano Colombo4.1 The evolving pharmaceutical field 854.2 Drug delivery and nanotechnology 894.3 Quality-by-design R&D 924.4 Artificial intelligence in drug delivery modeling 954.5 Artificial intelligence application in pharmaceutical product R&D 984.6 Landscape of AI implementation in the drug delivery industry 1094.7 Conclusion: the way forward 110References 1115. Cancer diagnostics and treatment decisions using artificial intelligence 117Reza Mirnezami5.1 Background 1175.2 Artificial intelligence, machine learning, and deep learning in cancer 1195.3 Artificial intelligence to determine cancer susceptibility 1225.4 Artificial intelligence for enhanced cancer diagnosis and staging 1255.5 Artificial intelligence to predict cancer treatment response 1275.6 Artificial intelligence to predict cancer recurrence and survival 1305.7 Artificial intelligence for personalized cancer pharmacotherapy 1335.8 How will artificial intelligence affect ethical practices and patients? 1365.9 Concluding remarks 137References 1396. Artificial intelligence for medical imaging 143Khanhvi Tran, Johan Peter Bøtker, Arash Aframian and Kaveh Memarzadeh6.1 Introduction 1436.2 Outputs of artificial intelligence in radiology/medical imaging 1446.3 Using artificial intelligence in radiology and overcoming its hurdles 1466.4 X-rays and artificial intelligence in medical imaging—case 1 (Zebra medicalvision) 1516.5 Ultrasound and artificial intelligence in medical imaging—case 2(Butterfly iQ) 1566.6 Application of artificial intelligence in medical imaging—case 3 (Arterys) 1586.7 Perspectives 160References 1617. Medical devices and artificial intelligence 163Arash Aframian, Farhad Iranpour and Justin Cobb7.1 Introduction 1637.2 The development of artificial intelligence in medical devices 1637.3 Limitations of artificial intelligence in medical devices 1717.4 The future frontiers of artificial intelligence in medical devices 172References 1748. Artificial intelligence assisted surgery 179Elan Witkowski and Thomas Ward8.1 Introduction 1798.2 Preoperative 1798.3 Intraoperative 1858.4 Postoperative 1938.5 Conclusion 196References 197Further reading 2029. Remote patient monitoring using artificial intelligence 203Zineb Jeddi and Adam Bohr9.1 Introduction to remote patient monitoring 2039.2 Deploying patient monitoring 2059.3 The role of artificial intelligence in remote patient monitoring 2099.4 Diabetes prediction and monitoring using artificial intelligence 2199.5 Cardiac monitoring using artificial intelligence 2219.6 Neural applications of artificial intelligence and remote patientmonitoring 2249.7 Conclusions 229References 23010. Security, privacy, and information-sharing aspects of healthcareartificial intelligence 235Jakub P. Hlávka10.1 Introduction to digital security and privacy 23510.2 Security and privacy concerns in healthcare artificial intelligence 23710.3 Artificial intelligence’s risks and opportunities for data privacy 24510.4 Addressing threats to health systems and data in the artificialintelligence age 25310.5 Defining optimal responses to security, privacy, and information-sharingchallenges in healthcare artificial intelligence 25510.6 Conclusions 263Acknowledgements 264References 26511. The impact of artificial intelligence on healthcare insurances 271Rajeev Dutt11.1 Overview of the global health insurance industry 27111.2 Key challenges facing the health insurance industry 27211.3 The application of artificial intelligence in the health insurance industry 27411.4 Case studies 28011.5 Moral, ethical, and regulatory concerns regarding the use of artificialintelligence 28011.6 The limitations of artificial intelligence 28211.7 The future of artificial intelligence in the health insurance industry 289References 29012. Ethical and legal challenges of artificial intelligence-drivenhealthcare 295Sara Gerke, Timo Minssen and Glenn Cohen12.1 Understanding “artificial intelligence” 29612.2 Trends and strategies 29612.3 Ethical challenges 30012.4 Legal challenges 30612.5 Conclusion 327Acknowledgements 328References 329Concluding remarks 337Index 339

Adam Bohr holds a PhD in Biomedical Engineering (2013) from University College London and has over a decade of experience in pharmaceutical and healthcare sciences across both academia and industry. He currently works at Novo Nordisk, where he leads AI and digital transformation initiatives in drug discovery and development. Dr. Bohr has a strong background in applying artificial intelligence to healthcare and in addition to his industry role, he is the founder and technical lead of Sonohaler, a company developing AI-driven solutions to support the management of respiratory conditions. His expertise spans the full spectrum of AI applications in healthcare, from early-stage research to real-world deployment. Dr. Bohr is also the co-editor of the book, Artificial Intelligence in Healthcare, which received wide recognition and readership and has a solid track record of peer-reviewed publications. Kaveh Memarzadeh, PhD is currently a Senior Manager at ChemoMetec, a biotechnology company that innovates in the field of automated cell cytometry. He holds research affiliations with University College London and Queen Mary University. He previously 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.

"This book is overall a very complete and organized book that gives newly interested persons a look at AI and its applications. The first few chapters give readers a chance to get used to the vocabulary and give small but effective diagrams explaining often confused concepts such as machine learning versus deep learning. The first few chapters may be extremely dry if the readers are fairly familiar with AI. Therefore, I would recommend this book for readers who feel unsure of their foundation in AI. I would say of the books I have read on digital health in general, this book is more focused. If you compare to Data Pulse: A Brief Tour of Artificial Intelligence in Healthcare, Marcetich (New Degree Press, 2020) or Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, Topal (Basic Books, 2019), both put a focus on challenges of AI, whereas this book has a large focus on the opportunities and covers them in great detail." --Doody Reviews

• List of contributors xiAbout the editors xiiiBiographies xvPreface xxiIntroduction xxiii1. Current healthcare, big data, and machine learning 1Adam Bohr and Kaveh Memarzadeh1.1 Current healthcare practice 11.2 Value-based treatments and healthcare services 51.3 Increasing data volumes in healthcare 101.4 Analytics of healthcare data (machine learning and deep learning) 161.5 Conclusions/summary 21References 222. The rise of artificial intelligence in healthcare applications 25Adam Bohr and Kaveh Memarzadeh2.1 The new age of healthcare 252.2 Precision medicine 282.3 Artificial intelligence and medical visualization 332.4 Intelligent personal health records 382.5 Robotics and artificial intelligence-powered devices 432.6 Ambient assisted living 462.7 The artificial intelligence can see you now 50References 573. Drug discovery and molecular modeling using artificial intelligence 61Henrik Bohr3.1 Introduction. The scope of artificial intelligence in drug discovery 613.2 Various types of machine learning in artificial intelligence 643.3 Molecular modeling and databases in artificial intelligence for drugmolecules 703.4 Computational mechanics ML methods in molecular modeling 723.5 Drug characterization using isopotential surfaces 743.6 Drug design for neuroreceptors using artificial neural network techniques 753.7 Specific use of deep learning in drug design 783.8 Possible future artificial intelligence development in drug design anddevelopment 80References 814. Applications of artificial intelligence in drug delivery and pharmaceutical development 85Stefano Colombo4.1 The evolving pharmaceutical field 854.2 Drug delivery and nanotechnology 894.3 Quality-by-design R&D 924.4 Artificial intelligence in drug delivery modeling 954.5 Artificial intelligence application in pharmaceutical product R&D 984.6 Landscape of AI implementation in the drug delivery industry 1094.7 Conclusion: the way forward 110References 1115. Cancer diagnostics and treatment decisions using artificial intelligence 117Reza Mirnezami5.1 Background 1175.2 Artificial intelligence, machine learning, and deep learning in cancer 1195.3 Artificial intelligence to determine cancer susceptibility 1225.4 Artificial intelligence for enhanced cancer diagnosis and staging 1255.5 Artificial intelligence to predict cancer treatment response 1275.6 Artificial intelligence to predict cancer recurrence and survival 1305.7 Artificial intelligence for personalized cancer pharmacotherapy 1335.8 How will artificial intelligence affect ethical practices and patients? 1365.9 Concluding remarks 137References 1396. Artificial intelligence for medical imaging 143Khanhvi Tran, Johan Peter Bøtker, Arash Aframian and Kaveh Memarzadeh6.1 Introduction 1436.2 Outputs of artificial intelligence in radiology/medical imaging 1446.3 Using artificial intelligence in radiology and overcoming its hurdles 1466.4 X-rays and artificial intelligence in medical imaging—case 1 (Zebra medicalvision) 1516.5 Ultrasound and artificial intelligence in medical imaging—case 2(Butterfly iQ) 1566.6 Application of artificial intelligence in medical imaging—case 3 (Arterys) 1586.7 Perspectives 160References 1617. Medical devices and artificial intelligence 163Arash Aframian, Farhad Iranpour and Justin Cobb7.1 Introduction 1637.2 The development of artificial intelligence in medical devices 1637.3 Limitations of artificial intelligence in medical devices 1717.4 The future frontiers of artificial intelligence in medical devices 172References 1748. Artificial intelligence assisted surgery 179Elan Witkowski and Thomas Ward8.1 Introduction 1798.2 Preoperative 1798.3 Intraoperative 1858.4 Postoperative 1938.5 Conclusion 196References 197Further reading 2029. Remote patient monitoring using artificial intelligence 203Zineb Jeddi and Adam Bohr9.1 Introduction to remote patient monitoring 2039.2 Deploying patient monitoring 2059.3 The role of artificial intelligence in remote patient monitoring 2099.4 Diabetes prediction and monitoring using artificial intelligence 2199.5 Cardiac monitoring using artificial intelligence 2219.6 Neural applications of artificial intelligence and remote patientmonitoring 2249.7 Conclusions 229References 23010. Security, privacy, and information-sharing aspects of healthcareartificial intelligence 235Jakub P. Hlávka10.1 Introduction to digital security and privacy 23510.2 Security and privacy concerns in healthcare artificial intelligence 23710.3 Artificial intelligence’s risks and opportunities for data privacy 24510.4 Addressing threats to health systems and data in the artificialintelligence age 25310.5 Defining optimal responses to security, privacy, and information-sharingchallenges in healthcare artificial intelligence 25510.6 Conclusions 263Acknowledgements 264References 26511. The impact of artificial intelligence on healthcare insurances 271Rajeev Dutt11.1 Overview of the global health insurance industry 27111.2 Key challenges facing the health insurance industry 27211.3 The application of artificial intelligence in the health insurance industry 27411.4 Case studies 28011.5 Moral, ethical, and regulatory concerns regarding the use of artificialintelligence 28011.6 The limitations of artificial intelligence 28211.7 The future of artificial intelligence in the health insurance industry 289References 29012. Ethical and legal challenges of artificial intelligence-drivenhealthcare 295Sara Gerke, Timo Minssen and Glenn Cohen12.1 Understanding “artificial intelligence” 29612.2 Trends and strategies 29612.3 Ethical challenges 30012.4 Legal challenges 30612.5 Conclusion 327Acknowledgements 328References 329Concluding remarks 337Index 339