Digital Pathology Podcast

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Is a digital slide as good as glass? Live from the Hamamatsu booth at PathVisions 2026, Dr. Aleks Zuraw and Amy Welch, Product Marketing Manager for NanoZoomer at Hamamatsu, open the question box and answer the beginner questions attendees dropped in all week. Elizabeth Blakely joins as a surprise guest.

You'll learn:

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What happens after your whole slide scanner arrives in the lab? Live from the Hamamatsu booth at PathVisions 2026, Dr. Aleks Zuraw meets the people who help labs go digital, plus a surprise guest from the DICOM standard.

In this episode:

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What are the biggest digital pathology trends of 2026, and what does Agilent buying Proscia tell us?

It's the clearest sign yet that the field is moving from shiny algorithms to full, connected workflows. In this live recap from Pathology Visions 2026 in San Diego, Dr. Aleks Zuraw (DVM, PhD, DACVP), board-certified veterinary pathologist and host of the Digital Pathology Podcast, sits down with Imogen Fitt of Signify Research to break down the biggest news of the week and the trends behind it.

Imogen tracks the digital pathology market for a...

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What if changing the scanner or choosing another slide from the same tumor changes your pathology AI results?

In DigiPath Digest #52, I review three studies on pathology foundation models, scanner shift, and slide selection to ask how much of “model performance” comes from the model itself.

We look at a benchmark comparing 32 foundation models across 41 tasks, a study testing 10 models across scanners and centers, and a breast cancer study examining how predictions change when different slides represent the same patient.

The findings challenge familiar assumptions about bigger mode...

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Can you use AI to write scientific papers without fabricated references, journal trouble or learning to code? I recorded this episode at the airport on my way to ESTP 2026 in Kraków. It's my practice run of the talk I gave there.

When deep learning came into pathology, people asked me if I was going to learn to code. I decided no. Large language models kind of proved me right. In this episode I walk you through how I use LLMs for writing papers, responding to co-authors, building slides and even r...

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What happens when AI models that perform almost perfectly on scattered cervical cells become less reliable than a coin flip on crowded cell groups?

In DigiPath Digest #51, I examine what this performance gap tells us about artificial intelligence in cytopathology.

The first paper evaluated six convolutional neural network models trained to distinguish benign from high-grade lesions using scattered cervical cytology cells. The models achieved AUCs ranging from 0.950 to 0.996 on the original images.

When the same models were applied to hyperchromatic crowded cell groups without retraining...

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What good is a powerful foundation model if it slows the pathologist down, can’t explain its result, or doesn’t fit the clinical workflow?

Foundation models are gaining attention across digital pathology. But they’re not finished clinical tools by themselves.

In this episode of the Digital Pathology Podcast, I speak with Panu Kauppila, Chief Product Officer at Aiforia, about what foundation models are, how they differ from traditional convolutional neural networks, and what it takes to make them useful for pathologists.

Panu describes a foun...

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Does more experience automatically make a cytotechnologist more accurate—or does where they look first matter more?

In DigiPath Digest #50, I review a digital cytology eye-tracking study that challenges the assumption that diagnostic accuracy improves steadily with years of practice.

The researchers tracked the visual behavior of 100 board-certified cytotechnologists with 1 to 40 years of experience. They found no statistically significant linear relationship between years of experience and diagnostic accuracy. Instead, low-power field efficiency—the ability to identify an important target quickly within a wider field—emerged as the key pr...

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Can a treatment decision depend on whether one pathologist sees 45% biomarker positivity and another sees 55%?

Visual immunohistochemistry scoring helped establish precision oncology. But as targeted therapies become more sensitive to subtle biological differences, categorical scores such as 0, 1+, 2+, and 3+ may no longer capture the information needed to identify the right patients.

In this episode, I speak with three Roche experts:

We discuss how computational pathology is changing companion...

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Is your digital pathology rollout moving so slowly that it’s creating a fragmented workflow instead of transforming the department?

In this episode of the Digital Pathology Podcast, I speak with Dr. Syed Hoda, Director of Digital Pathology at NYU, about why gradual implementation may no longer be the best approach to digital pathology adoption.

Dr. Hoda explains how NYU used an intensive nine-month planning period to prepare for a department-wide transition. The process involved pathology, IT, project managers, vendors, hospital leadership, and approximately 40–50 people participating in regu...

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If AI is already being used across the drug development pipeline, why hasn’t its impact matched the investment?

AI can help researchers review scientific literature, predict protein structures, prioritize molecules, assess toxicity, support clinical trials, and monitor adverse events. But access to better tools doesn’t automatically create better drugs.

In this episode, I speak with Thibault Geoui, Science CDO and host of the Tech & Drugs Podcast, about where AI is making a practical difference in drug discovery and development—and where the results remain...

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What does AI literacy actually look like for pathologists, researchers, and future clinicians? And how do you teach it in a way that is practical, not abstract?

In this episode, I talk with Candice Chu, DVM, PhD about something I think a lot of people in digital pathology and computational pathology are feeling right now: AI is moving fast, but education is still catching up.

Candice is a clinical pathologist, veterinarian, and educator building AI-focused teaching and research at Texas A&M. We worked...

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Are pathology foundation models actually ready for labs, or are they still stronger on paper than in practice?

In this episode of DigiPath Digest #49, I unpack a timely review on pathology foundation models and ask the question that matters most to me: not just what these models can do, but what has to be true before they are genuinely useful in real pathology workflows.

I walk through how pathology AI moved from narrow, task-specific models into the era of transformer-based foundation models. That shift matters...

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How far can pathologists take visual biomarker scoring before human vision becomes the bottleneck?

In this episode, I talk with Doug Bowman. PhD, VP Precision Medicine at Indica Labs, about what happens when companion diagnostics move from traditional visual scoring into the era of AI-powered image analysis. Doug comes from a biomedical and electrical engineering background, with experience in microscopy, digital image analysis, pharma workflows, and now precision medicine at Indica Labs. That combination makes him a great person to talk to about how image...

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Can AI copilots really keep up with pathologists when the cases are new, the workflow is messy, and the benchmark is actually protected from leakage?

In this episode of DigiPath Digest #48, I focus on one paper: DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset. I chose this paper because I think the field needs more of this kind of work. Less hype. More evaluation. Less “look what AI can do.” More “how do we test it in a way that actual...

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What happens when a pathology AI model misses the tissue before it even begins—and can better segmentation, education, and virtual staining close the gaps?

In DigiPath Digest #47, I review four recent studies that expose both the promise and the weak points of AI-assisted digital pathology. We start with a step that sounds simple: detecting tissue on a whole slide image. Yet when that first step fails, the downstream algorithm may miss cancer entirely.

From there, I look at a practical resource designed to improve communication between pa...

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Do you really need a scanner, whole slide images, and AI infrastructure before you can start in digital pathology?

In this episode, I argue that you do not.

I’m Dr. Aleksandra Zuraw, veterinary pathologist and digital pathology educator, and this talk is about a belief I hear all the time: I don’t have the tools yet, so there is no point learning digital pathology. I used to think that too. When I was training in Berlin, there was one Leica 6-slide scanner, and it felt like...

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Why are pathology vendors still speaking different image languages when radiology solved that problem decades ago?

In this episode of DigiPath Digest #46, I talk through four papers that all point to a bigger issue in digital pathology: we are not only dealing with better algorithms. We are dealing with interoperability, workflow design, explainability, and whether the field is actually ready to use these tools well.

I start with DICOM in digital pathology, because I think this is still one of the most important...

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What if the most frightening part of a pathology report is not the word cancer, but the silence that follows?

In this episode of the Digital Pathology Podcast, Dr. Aleksandra Zuraw talks with Michele Mitchell—breast cancer survivor, caregiver, national patient advocate, and longtime volunteer across Michigan Medicine, ASCP, the Digital Pathology Association, and MyPathologyReport.ca—about what happened when she saw her own cancer slide years after treatment. That moment changed how she understood her disease, her risk, and her role as a patient advocate.

Th...

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DigiPath Digest #45 asks a practical question: can AI in pathology move from correlation to real clinical use? In this episode, I review four papers that push on that question from different angles: computational pathology moving toward morphology-driven molecular inference, the current state of digital cytopathology and AI, multi-omics and precision oncology in hepatocellular carcinoma, and AI literacy in veterinary education. What ties them together is not model performance alone. It is the harder question of validation, workflow fit, quantitative use, ethics, and human oversight.

In the first...

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Where is AI in pathology actually becoming useful right now? In this episode of DigiPath Digest, I review 4 new PubMed papers across digital pathology, whole slide imaging (WSI), computational pathology, medical education, forensic pathology, and breast cancer AI. We look at a deep learning tool for coronary artery stenosis measurement in forensic autopsies, an AI-powered digital pathology model for renal pathology education, an open-source quality control tool for ...