Digital Pathology Podcast

Send us Fan Mail 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: David Clunie of DICOM Working Group 26 on the PathVisions 2026 DICOM Connectathon: scanners, archives, viewers and annotation tools from different vendors working together, and the new work on annotations with holesStephanie Fullerton introdu...

Send us Fan Mail 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 th...

Send us Fan Mail 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 whe...

Send us Fan Mail 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, buil...

Send us Fan Mail 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 o...