182: AI, Quality, and Standards: The Next Chapter of Digital Pathology
Sun Feb 08 2026
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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 matters18:10 – AI-generated tissue maps as metadata for WSI archives23:01 – PathQC: predicting RNA integrity and autolysis from H&E slides32:14 – ML-assisted IHC scoring in genitourinary cancers29:42 – Digital Pathology 101 book + community updatesKey Takeaways
Digital pathology adoption still requires clear standards and validation workflowsAI performs best when aligned with existing diagnostic frameworks (e.g., LI-RADS)Metadata extraction is a low-effort, high-impact AI use caseSlide-based quality control can support biobanking and biomarker researchAutomated IHC scoring improves consistency—but adoption remains uneven globallyResources 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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Send us a text 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 matters18:10 – AI-generated tissue maps as metadata for WSI archives23:01 – PathQC: predicting RNA integrity and autolysis from H&E slides32:14 – ML-assisted IHC scoring in genitourinary cancers29:42 – Digital Pathology 101 book + community updatesKey Takeaways Digital pathology adoption still requires clear standards and validation workflowsAI performs best when aligned with existing diagnostic frameworks (e.g., LI-RADS)Metadata extraction is a low-effort, high-impact AI use caseSlide-based quality control can support biobanking and biomarker researchAutomated IHC scoring improves consistency—but adoption remains uneven globallyResources 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/ Support the show Get the "Digital Pathology 101" FREE E-book and join us!