Digital Pathology: Implementation in Clinical Practice with AI Applications covers digital pathology applications in a clinical pathology laboratory setting, providing guidance and various tools for practical implementation. This comprehensive and detailed description of nuts and bolts of digital pathology implementation is unique and comes from the experience of high throughput scanning from a large surgical pathology laboratory. This is a valuable reference to prepare the end users not only about thoughtful launch of digital pathology but also to prepare for the future use of AI mediated decision support tools as the field advances. This book covers the entire range of this topic and can be used as a practical reference for individuals that wish to be part of the evolution of diagnostic pathology. Sections include pre-analytic, analytic, and post-analytic processes, encompassing hardware, software, space, staffing, quality control, laboratory information system applications, clinical validation, use cases and future applications, including computer assisted diagnosis and decision support tools.
1. Title of book - p. ii 2. Cover - p. ii 3. Title page - p. ii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. List of contributors - p. xiii 7. Preface - p. xv 8. Acknowledgment - p. xix 9. Chapter 1 Basics of digital pathology - p. 1 10. Chapter 2 The seven S’s for success - p. 23 11. Chapter 3 Building a business case for digital pathology - p. 47 12. Chapter 4 Preanalytic factors in digital pathology implementation - p. 79 13. Chapter 5 Quality management in digital pathology: analytic and postanalytic phases - p. 103 14. Chapter 6 Use cases in surgical pathology - p. 135 15. Chapter 7 Use cases in cytology - p. 171 16. Chapter 8 Regulatory aspects of digital pathology - p. 187 17. Chapter 9 Standards in digital pathology - p. 219 18. Chapter 10 The basics of machine learning and artificial intelligence - p. 243 19. Chapter 11 Academic industry partnerships - p. 257 20. Chapter 12 Ethical aspects of using artificial intelligence in digital and computational pathology - p. 267 21. Chapter 13 Next-generation imaging in pathology - p. 277 22. Index - p. 323
Digital Pathology: Implementation in Clinical Practice with AI Applications covers digital pathology applications in a clinical pathology laboratory setting, providing guidance and various tools for practical implementation. This comprehensive and detailed description of nuts and bolts of digital pathology implementation is unique and comes from the experience of high throughput scanning from a large surgical pathology laboratory. This is a valuable reference to prepare the end users not only about thoughtful launch of digital pathology but also to prepare for the future use of AI mediated decision support tools as the field advances. This book covers the entire range of this topic and can be used as a practical reference for individuals that wish to be part of the evolution of diagnostic pathology. Sections include pre-analytic, analytic, and post-analytic processes, encompassing hardware, software, space, staffing, quality control, laboratory information system applications, clinical validation, use cases and future applications, including computer assisted diagnosis and decision support tools.
1. Title of book - p. ii 2. Cover - p. ii 3. Title page - p. ii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. List of contributors - p. xiii 7. Preface - p. xv 8. Acknowledgment - p. xix 9. Chapter 1 Basics of digital pathology - p. 1 10. Chapter 2 The seven S’s for success - p. 23 11. Chapter 3 Building a business case for digital pathology - p. 47 12. Chapter 4 Preanalytic factors in digital pathology implementation - p. 79 13. Chapter 5 Quality management in digital pathology: analytic and postanalytic phases - p. 103 14. Chapter 6 Use cases in surgical pathology - p. 135 15. Chapter 7 Use cases in cytology - p. 171 16. Chapter 8 Regulatory aspects of digital pathology - p. 187 17. Chapter 9 Standards in digital pathology - p. 219 18. Chapter 10 The basics of machine learning and artificial intelligence - p. 243 19. Chapter 11 Academic industry partnerships - p. 257 20. Chapter 12 Ethical aspects of using artificial intelligence in digital and computational pathology - p. 267 21. Chapter 13 Next-generation imaging in pathology - p. 277 22. Index - p. 323
