Cargando…
Artificial intelligence in healthcare data /
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in t...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
Academic Press,
2020.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- <P>List of contributors xi</p> <p>About the editors xiii</p> <p>Biographies xv</p> <p>Preface xxi</p> <p>Introduction xxiii</p> <p>1. Current healthcare, big data, and machine learning 1</p> <p>Adam Bohr and Kaveh Memarzadeh</p> <p>1.1 Current healthcare practice 1</p> <p>1.2 Value-based treatments and healthcare services 5</p> <p>1.3 Increasing data volumes in healthcare 10</p> <p>1.4 Analytics of healthcare data (machine learning and deep learning) 16</p> <p>1.5 Conclusions/summary 21</p> <p>References 22</p> <p>2. The rise of artificial intelligence in healthcare applications 25</p> <p>Adam Bohr and Kaveh Memarzadeh</p> <p>2.1 The new age of healthcare 25</p> <p>2.2 Precision medicine 28</p> <p>2.3 Artificial intelligence and medical visualization 33</p> <p>2.4 Intelligent personal health records 38</p> <p>2.5 Robotics and artificial intelligence-powered devices 43</p> <p>2.6 Ambient assisted living 46</p> <p>2.7 The artificial intelligence can see you now 50</p> <p>References 57</p> <p>3. Drug discovery and molecular modeling using artificial intelligence 61</p> <p>Henrik Bohr</p> <p>3.1 Introduction. The scope of artificial intelligence in drug discovery 61</p> <p>3.2 Various types of machine learning in artificial intelligence 64</p> <p>3.3 Molecular modeling and databases in artificial intelligence for drug</p> <p>molecules 70</p> <p>3.4 Computational mechanics ML methods in molecular modeling 72</p> <p>3.5 Drug characterization using isopotential surfaces 74</p> <p>3.6 Drug design for neuroreceptors using artificial neural network techniques 75</p> <p>3.7 Specific use of deep learning in drug design 78</p> <p>3.8 Possible future artificial intelligence development in drug design and</p> <p>development 80</p> <p>References 81</p> <p>4. Applications of artificial intelligence in drug delivery and pharmaceutical development 85</p> <p>Stefano Colombo</p> <p>4.1 The evolving pharmaceutical field 85</p> <p>4.2 Drug delivery and nanotechnology 89</p> <p>4.3 Quality-by-design RD 92</p> <p>4.4 Artificial intelligence in drug delivery modeling 95</p> <p>4.5 Artificial intelligence application in pharmaceutical product RD 98</p> <p>4.6 Landscape of AI implementation in the drug delivery industry 109</p> <p>4.7 Conclusion: the way forward 110</p> <p>References 111</p> <p>5. Cancer diagnostics and treatment decisions using artificial intelligence 117</p> <p>Reza Mirnezami</p> <p>5.1 Background 117</p> <p>5.2 Artificial intelligence, machine learning, and deep learning in cancer 119</p> <p>5.3 Artificial intelligence to determine cancer susceptibility 122</p> <p>5.4 Artificial intelligence for enhanced cancer diagnosis and staging 125</p> <p>5.5 Artificial intelligence to predict cancer treatment response 127</p> <p>5.6 Artificial intelligence to predict cancer recurrence and survival 130</p> <p>5.7 Artificial intelligence for personalized cancer pharmacotherapy 133</p> <p>5.8 How will artificial intelligence affect ethical practices and patients? 136</p> <p>5.9 Concluding remarks 137</p> <p>References 139</p> <p>6. Artificial intelligence for medical imaging 143</p> <p>Khanhvi Tran, Johan Peter Bøtker, Arash Aframian and Kaveh Memarzadeh</p> <p>6.1 Introduction 143</p> <p>6.2 Outputs of artificial intelligence in radiology/medical imaging 144</p> <p>6.3 Using artificial intelligence in radiology and overcoming its hurdles 146</p> <p>6.4 X-rays and artificial intelligence in medical imaging-case 1 (Zebra medical</p> <p>vision) 151</p> <p>6.5 Ultrasound and artificial intelligence in medical imaging-case 2</p> <p>(Butterfly iQ) 156</p> <p>6.6 Application of artificial intelligence in medical imaging-case 3 (Arterys) 158</p> <p>6.7 Perspectives 160</p> <p>References 161</p> <p>7. Medical devices and artificial intelligence 163</p> <p>Arash Aframian, Farhad Iranpour and Justin Cobb</p> <p>7.1 Introduction 163</p> <p>7.2 The development of artificial intelligence in medical devices 163</p> <p>7.3 Limitations of artificial intelligence in medical devices 171</p> <p>7.4 The future frontiers of artificial intelligence in medical devices 172</p> <p>References 174</p> <p>8. Artificial intelligence assisted surgery 179</p> <p>Elan Witkowski and Thomas Ward</p> <p>8.1 Introduction 179</p> <p>8.2 Preoperative 179</p> <p>8.3 Intraoperative 185</p> <p>8.4 Postoperative 193</p> <p>8.5 Conclusion 196</p> <p>References 197</p> <p>Further reading 202</p> <p>9. Remote patient monitoring using artificial intelligence 203</p> <p>Zineb Jeddi and Adam Bohr</p> <p>9.1 Introduction to remote patient monitoring 203</p> <p>9.2 Deploying patient monitoring 205</p> <p>9.3 The role of artificial intelligence in remote patient monitoring 209</p> <p>9.4 Diabetes prediction and monitoring using artificial intelligence 219</p> <p>9.5 Cardiac monitoring using artificial intelligence 221</p> <p>9.6 Neural applications of artificial intelligence and remote patient</p> <p>monitoring 224</p> <p>9.7 Conclusions 229</p> <p>References 230</p> <p>10. Security, privacy, and information-sharing aspects of healthcare</p> <p>artificial intelligence 235</p> <p>Jakub P. Hlávka</p> <p>10.1 Introduction to digital security and privacy 235</p> <p>10.2 Security and privacy concerns in healthcare artificial intelligence 237</p> <p>10.3 Artificial intelligence's risks and opportunities for data privacy 245</p> <p>10.4 Addressing threats to health systems and data in the artificial</p> <p>intelligence age 253</p> <p>10.5 Defining optimal responses to security, privacy, and information-sharing</p> <p>challenges in healthcare artificial intelligence 255</p> <p>10.6 Conclusions 263</p> <p>Acknowledgements 264</p> <p>References 265</p> <p>11. The impact of artificial intelligence on healthcare insurances 271</p> <p>Rajeev Dutt</p> <p>11.1 Overview of the global health insurance industry 271</p> <p>11.2 Key challenges facing the health insurance industry 272</p> <p>11.3 The application of artificial intelligence in the health insurance industry 274</p> <p>11.4 Case studies 280</p> <p>11.5 Moral, ethical, and regulatory concerns regarding the use of artificial</p> <p>intelligence 280</p> <p>11.6 The limitations of artificial intelligence 282</p> <p>11.7 The future of artificial intelligence in the health insurance industry 289</p> <p>References 290</p> <p>12. Ethical and legal challenges of artificial intelligence-driven</p> <p>healthcare 295</p> <p>Sara Gerke, Timo Minssen and Glenn Cohen</p> <p>12.1 Understanding "artificial intelligence#x94; 296</p> <p>12.2 Trends and strategies 296</p> <p>12.3 Ethical challenges 300</p> <p>12.4 Legal challenges 306</p> <p>12.5 Conclusion 327</p> <p>Acknowledgements 328</p> <p>References 329</p> <p>Concluding remarks 337</p> <p>Index 339</p>