Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine and Liver Diseases: Concept, Technology, Application, and Perspectives combines four major applications of artificial intelligence (AI) within the field of clinical medicine specific to liver diseases: radiology imaging, electronic health records, pathology, and multiomics. The book provides a state-of-the-art summary of AI in precision medicine in hepatology, clarifying the concept and technology of AI and pointing to the current and future applications of AI within the field of hepatology. Coverage includes data preparation, methodology and application within disease-specific cases in fibrosis, viral and steatohepatitis, cirrhosis, hepatocellular carcinoma, acute liver failure, liver transplantation, and more. The ethical and legal issues of AI and future challenges and perspectives are also discussed.
By highlighting many new AI applications which can further research, diagnosis, and treatment, this reference is the perfect resource for both practicing hepatologists and researchers focused on AI applications in medicine.
1. Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine in Liver Diseases - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. List of contributors - p. Page iv 7. List of Illustrations - p. Page viii 8. List of Tables - p. Page ix 9. Chapter 1 : Artificial intelligence in health care: past and present - p. Page ix 10. Chapter 2 : Data-centric artificial intelligence in health care: progress, shortcomings, and remedies - p. Page 13 11. Chapter 3 : Artificial intelligence in radiology and its application in liver disease - p. Page 44 12. Chapter 4 : Electronic health record for artificial intelligence health care, and application to liver disease - p. Page 70 13. Chapter 5 : Artificial intelligence in pathology and application to liver disease - p. Page 89 14. Chapter 6 : Artificial intelligence using multiomics/genetic tools and application in liver disease - p. Page 103 15. Chapter 7 : Artificial intelligence in prediction of steatosis and fibrosis of nonalcoholic fatty liver disease - p. Page 125 16. Chapter 8 : Artificial intelligence in the prediction of progression and outcomes in viral hepatitis - p. Page 151 17. Chapter 9 : Artificial intelligence in cirrhosis complications and acute liver failure - p. Page 174 18. Chapter 10 : Artificial intelligence in liver transplantation - p. Page 192 19. Chapter 11 : Artificial intelligence in liver cancer: diagnosis and management - p. Page 208 20. Chapter 12 : Predicting drug-induced liver injury with artificial intelligence—a minireview - p. Page 226 21. Chapter 13 : Artificial intelligence in precision medicine and liver disease monitoring - p. Page 246 22. Chapter 14 : Regulatory, social, ethical, and legal issues of artificial intelligence in medicine - p. Page 265 23. Chapter 15 : Opportunities and challenges of explainable artificial intelligence in medicine: toward causability for physicians, developers, and patients - p. Page 278 24. Chapter 16 : Outlook of future landscape of artificial intelligence in health care of liver disease and challenges - p. Page 299 25. Index - p. Page 319
Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine and Liver Diseases: Concept, Technology, Application, and Perspectives combines four major applications of artificial intelligence (AI) within the field of clinical medicine specific to liver diseases: radiology imaging, electronic health records, pathology, and multiomics. The book provides a state-of-the-art summary of AI in precision medicine in hepatology, clarifying the concept and technology of AI and pointing to the current and future applications of AI within the field of hepatology. Coverage includes data preparation, methodology and application within disease-specific cases in fibrosis, viral and steatohepatitis, cirrhosis, hepatocellular carcinoma, acute liver failure, liver transplantation, and more. The ethical and legal issues of AI and future challenges and perspectives are also discussed.
By highlighting many new AI applications which can further research, diagnosis, and treatment, this reference is the perfect resource for both practicing hepatologists and researchers focused on AI applications in medicine.
1. Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine in Liver Diseases - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. List of contributors - p. Page iv 7. List of Illustrations - p. Page viii 8. List of Tables - p. Page ix 9. Chapter 1 : Artificial intelligence in health care: past and present - p. Page ix 10. Chapter 2 : Data-centric artificial intelligence in health care: progress, shortcomings, and remedies - p. Page 13 11. Chapter 3 : Artificial intelligence in radiology and its application in liver disease - p. Page 44 12. Chapter 4 : Electronic health record for artificial intelligence health care, and application to liver disease - p. Page 70 13. Chapter 5 : Artificial intelligence in pathology and application to liver disease - p. Page 89 14. Chapter 6 : Artificial intelligence using multiomics/genetic tools and application in liver disease - p. Page 103 15. Chapter 7 : Artificial intelligence in prediction of steatosis and fibrosis of nonalcoholic fatty liver disease - p. Page 125 16. Chapter 8 : Artificial intelligence in the prediction of progression and outcomes in viral hepatitis - p. Page 151 17. Chapter 9 : Artificial intelligence in cirrhosis complications and acute liver failure - p. Page 174 18. Chapter 10 : Artificial intelligence in liver transplantation - p. Page 192 19. Chapter 11 : Artificial intelligence in liver cancer: diagnosis and management - p. Page 208 20. Chapter 12 : Predicting drug-induced liver injury with artificial intelligence—a minireview - p. Page 226 21. Chapter 13 : Artificial intelligence in precision medicine and liver disease monitoring - p. Page 246 22. Chapter 14 : Regulatory, social, ethical, and legal issues of artificial intelligence in medicine - p. Page 265 23. Chapter 15 : Opportunities and challenges of explainable artificial intelligence in medicine: toward causability for physicians, developers, and patients - p. Page 278 24. Chapter 16 : Outlook of future landscape of artificial intelligence in health care of liver disease and challenges - p. Page 299 25. Index - p. Page 319
