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 Why does digital pathology adoption move faster in some places than others? 

In this USCAP 2026 conversation, I sat down with Robert Moody and Fumiya Fuji from Hamamatsu to talk about what the conference theme, MAKING CONNECTIONS, really looks like in practice. This was not just a scanner conversation. It was a workflow conversation. 

We talked about why digital pathology has shifted from a scanner-first mindset to a solution-first one, and why that matters for labs trying to build workflows tha...

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What makes digital pathology feel so hard to enter, even for smart people already working around it?

In this special USCAP conversation, Stephanie Fullerton from Hamamatsu turns the tables and interviews me about Digital Pathology 101 — the book I wrote for people who are starting or continuing their digital pathology journey.

We talk about why the book is not meant to be an exhaustive manual, but a practical framework. A way to help people see the full picture, ask better questions, and understand how the pieces of...

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What happens when AI looks strong in a paper, but the workflow still isn’t ready?

In DigiPath Digest #40, I reviewed five recent papers across kidney pathology, oral and maxillofacial pathology, glioma biomarker prediction, digital twins in neuro-oncology, and a major European colorectal cancer cohort. A common theme kept coming back: good performance is not the same thing as real-world readiness.

We started with kidney biopsies and the challenge of assessing interstitial fibrosis and tubular atrophy, where AI shows promise but still does not fully ag...

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If AI can detect patterns we cannot see, how do we know when its answers are clinically trustworthy?

In this episode of DigiPath Digest #39, I explore a big-picture question in digital pathology and medical AI. Many models now match or even exceed human performance in specific diagnostic tasks. But most of that evidence comes from controlled or retrospective datasets. So what happens when we try to bring these tools into real clinical workflows?

I review four recent papers that help frame this challenge and point toward...

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Clinical Artificial Intelligence in 2026. Accuracy, Education, and Guardrails

Artificial intelligence is evolving fast in medicine. But how accurate is it. And are we building it safely?

In this episode of DigiPath Digest, I review five new studies shaping digital pathology, radiology, burn diagnostics, and agent-based large language model systems. We discuss accuracy gains, hallucination filtering, education challenges, and why safeguards are essential before clinical deployment.

Clear. Practical. Evidence-based.

⏱ Topics & Timestamps

[00:02] Introduction
Weekly journal club on digital pathology and artificial in...

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What if one of the biggest sources of diagnostic variability in prostate cancer isn’t the pathologist—but the stain we’ve trusted for decades?

In this episode, I speak with Professor Ingid Carlbom, founder of CADESS.AI, about a different way to approach prostate cancer grading—by rethinking staining, segmentation, and AI decision support from the ground up. We explore why 30–40% interobserver variability persists in Gleason grading and how optimized stains combined with explainable AI can significantly reduce that uncertainty.

Ingrid shares her journey from applied mathematics and computer science in...

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Sometimes a paper comes out that’s so practical and relevant to what we do in digital pathology that I know we have to talk about it.

In this episode, I dive into “A Guide for the Deployment, Validation and Accreditation of Clinical Digital Pathology Tools” from Geneva University Hospital (HUG) — one of the most useful, real-world frameworks I’ve seen for bringing digital pathology tools safely into clinical practice.

If you’ve ever built an AI model and wondered, “Now what?”, this episode is for you.
Because building...

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Is AI in pathology actually improving diagnosis — or just adding complexity?

In DigiPath Digest #37, we reviewed four recent publications covering AI-based biomarker quantification in glioblastoma, real-world digital workflow integration in prostate cancer, multimodal AI combining histopathology and genomics, and patient perspectives on AI in cancer diagnostics.

This episode connects technical performance with something equally important: trust.

Episode Highlights

[00:02] Community & updates
Digital Pathology 101 free PDF, upcoming patient-focused book, and global attendance.

[04:07] AI-based image analysis in glioblastoma
AI sh...

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Get the "Digital Pathology 101" FREE E-book and join us!

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What actually needs to be in place before digital pathology can replace the microscope?

In this episode of DigiPath Digest, I walk through the 2026 Polish Society of Pathologists guidelines and translate them into practical steps for real pathology labs. This isn’t theory. It’s about hardware fidelity, data integrity, validation, and AI integration — and what each of these actually requires in daily workflow.

We talk about scanner resolution standards (≤0.26 μm per pixel), 4K monitor calibration, visually lossless compression (20:1), scalable storage, pathologist-driven validation, and what “non-inferiority” truly means.<...

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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 st...

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What happens when artificial intelligence moves beyond images and begins interpreting clinical notes, kidney biopsies, multimodal cancer data, and even healthcare costs?

In this episode, I open the year by exploring four recent studies that show how AI is expanding across the full spectrum of medical data. From Large Language Models (LLM) reading unstructured clinical text to computational pathology supporting rare kidney disease diagnosis, multimodal cancer prediction, and cost-effectiveness modeling in oncology, this session connects innovation with real-world clinical impact.

Across all discussions, one theme is clear: progress...

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What really changed in digital pathology this year—and what still needs work? 

As we close out 2025 and step into 2026, I wanted to pause, reflect, and share what I’ve seen shift from theory to real-world practice across labs, conferences, and clinical workflows.

I look back at the most meaningful developments in digital pathology and AI in 2025—from wider adoption of primary diagnosis on digital slides to more grounded, evidence-driven use of AI tools. We’ve moved past hype and pilots and started asking harder questions about val...

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What if the biggest breakthrough in pathology AI isn’t a new algorithm—but finally sharing the data we already have?

In this episode, I’m joined by Jeroen van der Laak and Julie Boisclair from the IMI BigPicture consortium, a European public-private initiative building one of the world’s largest digital pathology image repositories. The goal isn’t to create a single AI model—but to enable thousands by making high-quality, legally compliant data accessible at scale.

We unpack what it really takes to build a 3-million-slide repository across 44 partners, wh...

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What if the biggest transformation in digital pathology this year had nothing to do with new hardware—and everything to do with how we think about value, workflow, and readiness?

In this year-end recap livestream from the 11th Digital Pathology & AI Congress in London, I break down what truly mattered in 2025. Instead of focusing on buzzwords or hype cycles, this episode highlights the practical advances shaping diagnostics, patient care, and drug development—and the mindset shift our field must embrace to move forward.

Digital pathology is no longer “early...

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Have you ever thought, “Digital pathology sounds amazing, but without a scanner, what’s the point of learning it now?”
 If so, this episode will change how you see your role in the future of pathology.

In this talk, I challenge one of the most persistent myths in our field: the belief that you need expensive hardware before you can begin your digital pathology journey. Through personal experience and the remarkable story of another pathologist who started with even less, I show why knowledge—not infrastructure—is what truly opens doors.

Hig...