182: AI, Quality, and Standards: The Next Chapter of Digital Pathology

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This session is a practical walkthrough of where digital pathology and AI truly stand in early 2026—based on five recent PubMed papers and real-world implementation experience.

In this episode, I review new clinical adoption guidelines, AI applications in liver cancer imaging and pathology, AI-ready metadata for whole slide images, non-destructive tissue quality control from H&E slides, and machine learning–assisted IHC scoring in precision oncology.

This conversation is not about hype. It’s about standards, validation, data integrity, and clinical translation—the factors that decide whether AI tools stay in research or reach patient care.

Episode Highlights

• 01:21 – Practical digital pathology adoption guidelines (Polish Society of Pathologists)
• 08:05 – AI in liver cancer imaging & pathology, and why framework alignment matters
• 18:10 – AI-generated tissue maps as metadata for WSI archives
• 23:01 – PathQC: predicting RNA integrity and autolysis from H&E slides
• 32:14 – ML-assisted IHC scoring in genitourinary cancers
• 29:42 – Digital Pathology 101 book + community updates

Key Takeaways

• Digital pathology adoption still requires clear standards and validation workflows
• AI performs best when aligned with existing diagnostic frameworks (e.g., LI-RADS)
• Metadata extraction is a low-effort, high-impact AI use case
• Slide-based quality control can support biobanking and biomarker research
• Automated IHC scoring improves consistency—but adoption remains uneven globally

Resources Mentioned 

• Digital Pathology 101 (free PDF & audiobook)

Publication Links:  a. https://pubmed.ncbi.nlm.nih.gov/41618426/                                                                 b. https://pubmed.ncbi.nlm.nih.gov/41616271/                                                                   c. https://pubmed.ncbi.nlm.nih.gov/41610818/                                                                 d. https://pubmed.ncbi.nlm.nih.gov/41595938/                                                                 e. https://pubmed.ncbi.nlm.nih.gov/41590351/ 

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