Digital Pathology Podcast

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...

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 lesion...

Send us Fan Mail 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...

Send us Fan Mail 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...

Send us Fan Mail 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 patie...

Send us Fan Mail 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 in...

Send us Fan Mail 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 wi...

Send us Fan Mail 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. Cand...

Send us Fan Mail 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...

Send us Fan Mail 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, wit...

Send us Fan Mail 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...

Send us Fan Mail 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...

Send us Fan Mail 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...

Send us Fan Mail 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 a...

Send us Fan Mail 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...

Send us Fan Mail 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 car...

Send us Fan Mail 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 d...

Send us Fan Mail Paper Discussed in this Episode: Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings. Bedőházi Z, Biricz A, Kilim O, et al. Journal of Pathology Informatics 21 (2026) 100644. Episode Summary: Welcome back, Trailblazers! In this Journal Club deep dive of the Digital Pathology Podcast, we flip the core assumptio...

Send us Fan Mail Paper Discussed in this Episode: Integrating AI-Powered Digital Pathology With Case-Based Teaching: A Novel Paradigm for Renal Education in Medical School. Zhou H, Cui L. Clin Teach 2026; 23(3):e70421. doi: 10.1111/tct.70421. Episode Summary: In this journal club episode tailored for healthcare trailblazers, we explore a massive paradigm shift in medical education. We examine a 20...

Send us Fan Mail Paper Discussed in this Episode: Spatial omics and AI for clinically actionable cancer biomarkers. Reitsam NG. PLoS Med 2026; 23(4): e1005049. Episode Summary: In this deep dive, we explore how artificial intelligence and spatial omics are fundamentally rewriting the rules of cancer diagnostics. We break down a 2026 editorial that challenges a deceptively simple question driving m...

Send us Fan Mail Paper Discussed in this Episode: Artificial intelligence in clinical oncology: Multimodal integration and translational development. Ruichong Lin, Zhenhui Zhao, Zhonghai Liu, Jin Kang, Kang Zhang, Xiaoying Huang, Yunfang Yu. Cancer Letters 2026; Volume 649, 218493. Episode Summary: In this journal club deep dive, we explore how cutting-edge AI is fundamentally rewriting the rules...

Send us Fan Mail Paper Discussed in this Episode: Advancements in bone marrow biopsy: the role of omics and artificial intelligence in hematologic diagnostics. Maryam Alwahaibi and Nasar Alwahaibi. Front. Med. 2026; 13:1772478. Episode Summary: In this journal club deep dive, we explore a paradigm shift in hematopathology, moving from 19th-century visual assessments to the cutting edge of precisio...

Send us Fan Mail I did something I've never done before for this episode — I went live from the middle of a national park. This is DigiPath Digest #42, broadcasting from the Great Sand Dunes National Park in Colorado via Starlink from my family road trip. Yes, it actually worked. And so did the papers. This episode covers four papers that all ask the same uncomfortable question from different...

Send us Fan Mail Paper Discussed in this Episode: How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer. T. Ruelle, T. Grinda, L. Del Mastro, M. Lacroix-Triki, B. Pistilli & G. Gessain. ESMO Real World Data and Digital Oncology 2026. Episode Summary: In this journal club episode, we step into the reality of computational pathology and e...

Send us Fan Mail Paper Discussed in this Episode: Artificial intelligence in oral oncology: Current advances and future potential in diagnosis, prognosis, and therapeutic decision-making. Annamalai A, Dhanes V, Jayalakshmi L, Shanmugam R, Ravi S. Cancer Treatment and Research Communications 47 (2026) 101193. Episode Summary: In this journal club deep dive, we explore how AI is fundamentally reshap...