AI in Digital Pathology
From Implementation to Impact
- 1st Edition - June 1, 2027
- Latest edition
- Authors: Noor Shaker, Nuha Shaker, Mo AbouZleikha
- Language: English
AI in Digital Pathology: From Implementation to Impact offers a comprehensive, practical guide bridging AI engineering and clinical pathology. Authored by AI experts and a practi… Read more
Description
Description
AI in Digital Pathology: From Implementation to Impact offers a comprehensive, practical guide bridging AI engineering and clinical pathology. Authored by AI experts and a practicing pathologist, the book covers the full spectrum of AI in pathology—from whole-slide imaging pipelines and data handling to advanced AI methods like transformers and multimodal models. It addresses technical challenges such as stain variability and annotation strategies, alongside validation, deployment, and regulatory considerations. Featuring real-world case studies and best practices, it equips AI engineers, pathologists, hospitals, and academic institutions with the knowledge to build, evaluate, and responsibly adopt AI tools that improve diagnostics, prognostics, and treatment personalization. Structured to reflect real clinical workflows, this resource fills a critical gap by combining deep technical detail with clinical insights, making it essential for advancing AI integration in pathology practice.
Key features
Key features
- Offers end-to-end technical coverage of AI implementation in pathology, from whole-slide imaging pipelines to model deployment
- Provides comprehensive case studies and clinical examples that bridge research with everyday pathology practice
- Includes practical tips, evaluation strategies, and lessons learned from real-world clinical deployments to accelerate adoption and reduce failure
- Presents in-depth explanations of advanced AI methods, including supervised, self-supervised learning, transformers, and multimodal models
Readership
Readership
Computational pathology engineers, Pathologists, Lab directors Hospitals and Contract Research Organizations (CROs)
Table of contents
Table of contents
Section 1: Introduction to AI in Pathology
1.The Role of Pathology in Medicine
2.Opportunities and Challenges for AI in Pathology
3.Overview of AI Technologies in Pathology
Section 2: Data Foundations and Preprocessing
4. Whole-Slide Imaging Pipelines
5. Stain Variability and Normalization
6. Annotation Strategies and Data Labeling
7. Multi-Institutional Data Integration
Section 3: AI Learning Paradigms and Model Architectures
8. Supervised Learning in Pathology
9. Weakly Supervised and Self-Supervised Learning
10. Transformer Models and Attention Mechanisms
11. Multimodal AI Models
Section 4: Model Validation and Deployment
12. Evaluation Metrics and Validation Strategies
13. Regulatory Considerations and Compliance
14. Deployment in Clinical Workflows
Section 5: Clinical Applications of AI in Pathology
15. Diagnostic Applications and Tumor Grading
16. Prognostic Models and Biomarker Prediction
17. Patient Stratification and Treatment Management
18. Workflow Optimization and Triage
Section 6: System-Level Perspectives and Future Directions
19. Precision Medicine and AI Integration
20. Economic and Ethical Considerations
21. Emerging Trends and Future Challenges
1.The Role of Pathology in Medicine
2.Opportunities and Challenges for AI in Pathology
3.Overview of AI Technologies in Pathology
Section 2: Data Foundations and Preprocessing
4. Whole-Slide Imaging Pipelines
5. Stain Variability and Normalization
6. Annotation Strategies and Data Labeling
7. Multi-Institutional Data Integration
Section 3: AI Learning Paradigms and Model Architectures
8. Supervised Learning in Pathology
9. Weakly Supervised and Self-Supervised Learning
10. Transformer Models and Attention Mechanisms
11. Multimodal AI Models
Section 4: Model Validation and Deployment
12. Evaluation Metrics and Validation Strategies
13. Regulatory Considerations and Compliance
14. Deployment in Clinical Workflows
Section 5: Clinical Applications of AI in Pathology
15. Diagnostic Applications and Tumor Grading
16. Prognostic Models and Biomarker Prediction
17. Patient Stratification and Treatment Management
18. Workflow Optimization and Triage
Section 6: System-Level Perspectives and Future Directions
19. Precision Medicine and AI Integration
20. Economic and Ethical Considerations
21. Emerging Trends and Future Challenges
Product details
Product details
- Edition: 1
- Latest edition
- Published: June 1, 2027
- Language: English
About the authors
About the authors
Noor Shaker
Author
Dr. Noor Shaker is a distinguished AI expert and entrepreneur specializing in artificial intelligence applications in healthcare and life sciences. Currently, she is the Founder and CEO of SpatialX (AI in Digital Pathology), a UK-based company developing AI tools for cancer diagnostics in collaboration with leading hospitals. Previously, she led Glamorous AI as Founder and CEO, which was acquired by X-Chem Inc., where she served as SVP and General Manager. Noor also co-founded GTN Ltd, focusing on quantum machine learning for medicine discovery. With a Ph.D. in Computer Science from IT University of Copenhagen, she has held academic positions including Assistant Professor and Postdoctoral Fellow. Noor has published over 70 papers, holds multiple patents, and has received numerous awards such as the MIT Innovators 35 under 35 and BBC 100 Women. She actively contributes to AI strategy on boards including Enko Inc. and advisory roles at Coventry University and the Rosalind Franklin Institute.
Affiliations and expertise
University of Pittsburgh Medical CenterNuha Shaker
Author
Dr. Nuha Shaker is a Board-Certified Pathologist experienced in applying and validating AI tools for diagnostic and prognostic use. This author brings a clinical perspective on how AI integrates into pathology workflows in a practical way. Dr. Nuha Shaker is an Assistant Professor specializing in Gastrointestinal, Pancreatic, and Hepatobiliary Pathology at the University of Pittsburgh Medical Center. She completed her pathology residency at the University of Kentucky. Dr. Shaker has contributed extensively to pathology research, authoring numerous peer-reviewed publications focused on cancer diagnostics and prognostic biomarkers. She is an active member of professional societies including USCAP, CAP, ASCO, and AGA. Her work integrates advanced techniques such as AI-driven histological analysis to improve diagnostic accuracy and patient outcomes. Dr. Shaker has received multiple research awards and grants, reflecting her commitment to advancing precision oncology and pathology.
Affiliations and expertise
Board-certified pathologist at the University of Pittsburgh Medical CenterMo AbouZleikha
Author
Mo AbouZleikha Co-founder of Glamorous AI with over 20 years of experience in AI. He has built AI products for drug discovery and computational pathology and scaled platforms for use by thousands of scientists and clinicians. He brings deep expertise in engineering scalable AI systems and products is a seasoned AI technology leader with over 20 years of experience in AI strategy, product development, and team leadership. He is the Founding Director of Deep Delta AI, providing consultancy in AI strategy, scalable system design, and team coaching. Previously, as VP and Head of AI at X-Chem Inc., he led the UK subsidiary, driving AI innovation in drug discovery with transformative models that significantly accelerated hit identification and reduced costs. Holding a Ph.D. in Computer Science from University College Dublin, his expertise bridges AI and drug discovery, enabling impactful cross-functional collaboration.
Affiliations and expertise
Deep Delta AI