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...

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 foundation models are, how they differ from traditional convolutional neural networks, and what i...

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 years of experience. They found no statistically significant linear relationship between years of...

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 patients. In this episode, I speak with three Roche experts: Gordana Juric-Sekhar, MD, anatomic pathol...

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 that first step fails, the downstream algorithm may miss cancer entirely. From there, I look at a...

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 intensive nine-month planning period to prepare for a department-wide transition. The process invol...

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 with Thibault Geoui, Science CDO and host of the Tech & Drugs Podcast, about where AI is making...

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. Candice is a clinical pathologist, veterinarian, and educator building AI-focused teaching and r...

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 through how pathology AI moved from narrow, task-specific models into the era of transformer-based ...

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, with experience in microscopy, digital image analysis, pharma workflows, and now precision medicine at...

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 kind of work. Less hype. More evaluation. Less “look what AI can do.” More “how do we test it in a...

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 that too. When I was training in Berlin, there was one Leica 6-slide scanner, and it felt like d...

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 actually ready to use these tools well. I start with DICOM in digital pathology, because I think th...

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.ca—about what happened when she saw her own cancer slide years after treatment. That moment chang...

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 carcinoma, and AI literacy in veterinary education. What ties them together is not model performance...

Send us Fan Mail Paper Discussed in this Episode: High-Sensitivity Pan-Cancer AI Assessment of Lymph Node Metastasis via Uncertainty Quantification. Wang X, Chen Y, Liu X, et al. npj Digit. Med. (2026). Episode Summary: In this episode, we explore a groundbreaking 2026 study that tackles the "black box" problem of medical AI. We dive into UPATHLN, a pan-cancer AI platform for detecting lymph node metastases that doesn't just try to be right—it explicitly knows when it might be wrong. By using...

Send us Fan Mail Paper Discussed in this Episode: Reliable classification of polyps based on artificial intelligence: a development and validation study. Julbø FMI, Henriksen AL, et al. eClinicalMedicine 2026;93: 103826. Episode Summary: In this journal club deep dive, we explore a groundbreaking 2026 study that tackles the massive bottleneck in gastrointestinal pathology caused by successful colorectal screening programs. We examine POLARIS, an AI triage system designed to safely clear over ...

Send us Fan Mail Paper Discussed in this Episode: A Deep Learning Framework for Automated Triage of Breast Cancer Biopsies in Malaysia: A Simulation Study to Reduce Resource Consumption and Diagnostic Turnaround Time. Yudi Kurniawan Budi Susilo, Dewi Yuliana, Shamima Abdul Rahman, Siew Lian Leong. Clinical Breast Cancer 2026 . Episode Summary: In this deep dive, we explore a revolutionary approach to a massive real-world healthcare bottleneck: agonizingly long diagnostic wait times in resourc...

Send us Fan Mail Paper Discussed in this Episode: A Deep Learning Framework for Automated Triage of Breast Cancer Biopsies in Malaysia: A Simulation Study to Reduce Resource Consumption and Diagnostic Turnaround Time. Susilo YKB, Yuliana D, Rahman SA, Leong SL. Clinical Breast Cancer 2026. Episode Summary: In this journal club deep dive on the Digital Pathology Podcast, we explore a 2026 study tackling severe diagnostic bottlenecks in breast cancer care. Facing a critical shortage of patholog...

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 assumption of microscopic precision on its head. Can an AI accurately predict pathological breast cancer s...

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 2026 perspective article that uses the notoriously complex field of renal pathology as a stress tes...

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 precision medicine. We examine a 2026 review that unpacks how combining artificial intelligence with mult...

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 of cancer diagnostics. We examine a comprehensive 2026 review on clinical oncology that highlight...

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 modern oncology: Is a tumor "positive" or "negative" for a biomarker? As targeted cancer therapies...

Send us Fan Mail Paper Discussed in this Episode: How to bring generative AI to oncology practice. D. Truhn & J. N. Kather. ESMO Real World Data and Digital Oncology 2026. Episode Summary: In this journal club deep dive, we step out of the theoretical sci-fi hype of artificial intelligence and look at a practical, real-world roadmap for bringing Generative AI into oncology. We examine a 2026 paper that maps out the trajectory for deploying Large Language Models (LLMs) to combat the overwh...

Send us Fan Mail Paper Discussed in this Episode: Can large language models like ChatGPT and Gemini interpret cervical cytology accurately? Saroja Devi Geetha. Annals of Diagnostic Pathology 2026; Volume 83, 152641. Episode Summary: In this journal club deep dive, we explore what happens when advanced artificial intelligence is thrown into the visually chaotic realm of human biology. We examine a 2026 study evaluating whether two massive multimodal models—GPT-5 and Gemini 2.5 Pro—can accurate...

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 reshaping the clinical management of Oral Squamous Cell Carcinoma (OSCC). We examine a comprehensive Ma...

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 explore how artificial intelligence is fundamentally transforming breast cancer diagnostics. We ex...

Send us Fan Mail Paper Discussed in this Episode: Ki-67 Proliferation Index in Pulmonary Neuroendocrine Neoplasms: Interobserver Agreement Among Pathologists and Comparison of Two Artificial Intelligence-Based Image Analysis Systems. Teoman G, Turkmen Usta Z, Sagnak Yilmaz Z, Ersoz S. MDPI 2026. Episode Summary: In this journal club deep dive, we step into the lab to examine a direct comparison between expert human pathologists and artificial intelligence. We explore a 2026 study that evaluat...

Send us Fan Mail Deep Learning Can Predict the Overall Survival of Cervical Cancer Based on Histopathological Image, Gene Mutation and Clinical Information. Shen J, Miao Z, Wang L, et al. IET Systems Biology 2026. Episode Summary: In this deep dive, we explore a groundbreaking 2026 study that uses multimodal deep learning to act as a "master diagnostician" for cervical cancer. We examine what happens when an AI is fed a combination of standard clinical data, cutting-edge genetic sequencing, a...

Send us Fan Mail Paper Discussed in this Episode: Modern Pathology-Driven Strategies in Neoadjuvant Immunotherapy for Head and Neck Squamous Cell Carcinoma: From Residual Tumor Quantification to Spatial and AI-Based Biomarkers. Annabella Di Mauro, Rossella De Cecio, Saverio Simonelli, et al. Cancers (MDPI) 2026. Episode Summary: In this journal club deep dive, we explore a paradigm-shifting 2026 paper that fundamentally fractures our reliance on traditional radiology in head and neck cancer. ...

Send us Fan Mail Paper Discussed in this Episode: Molecular Pathology, Artificial Intelligence, and New Technologies in Hematologic Diagnostics: Translational Opportunities and Practical Considerations. Alnoor F, Mukherjee S, Menon MP, Ng D, Li P, Ohgami RS. Diagnostics 2026. Episode Summary: In this deep dive, we explore how hematology labs are tackling a massive rise in diagnostic complexity combined with persistent staffing shortages. The solution isn't just working harder—it's an entirely...

Send us Fan Mail Paper Discussed in this Episode: Quantitative regression of qFibrosis with resmetirom: Exploratory histologic endpoints from the MAESTRO-NASH phase III clinical trial. Schattenberg JM, Bedossa P, Guy CD, et al. Journal of Hepatology 2026; https://doi.org/10.1016/j.jhep.2026.03.021. Episode Summary: In this deep dive, we explore how artificial intelligence is revolutionizing the way we measure liver disease recovery. We examine a groundbreaking 2026 Phase III clinical trial (M...

Send us Fan Mail Paper Discussed in this Episode: Digital Twins in Neuro-Oncology: A Systematic Review of Current Implementations, Technical Strategies, and Clinical Applications. Annie Singh, Fatima Ahmad Qureshy, Angelica Kurtz, Moinak Bhattacharya, Prateek Prasanna, and Gagandeep Singh. Radiology: Imaging Cancer 2026; 8(2). Episode Summary: In this journal club deep dive, we explore a groundbreaking 2026 systematic review of digital twins in neuro-oncology. We step past the buzzwords and e...

Send us Fan Mail Paper Discussed in this Episode: A comprehensive European Colorectal Cancer Cohort dataset. Holub P, Törnwall O, Garcia Alvarez E, et al. Sci Data (2026). https://doi.org/10.1038/s41597-026-06822-2. Episode Summary: In this journal club edition of the Digital Pathology Podcast, we explore a monumental effort to clear up the diagnostic "muddy waters" of Colorectal Cancer (CRC). We examine a groundbreaking 2026 paper detailing a massive European dataset of 10,780 CRC patients t...

Send us Fan Mail Paper Discussed in this Episode: The Performance of Artificial Intelligence in Classifying Molecular Markers in Adult-Type Gliomas Using Histopathological Images: Systematic Review. Almaabreh O, Al-Dafi R, Tabassum A, Othman A, Abd-alrazaq A. J Med Internet Res 2026; 28: e78377. Episode Summary: In this deep dive of the Digital Pathology Podcast, we explore the intersection of human limitations and computational power. Following the 2021 World Health Organization mandate requ...

Send us Fan Mail Paper Discussed in this Episode: Artificial Intelligence and Its Applications in Oral and Maxillofacial Pathology. Veremis B. Dent Clin North Am. 2026 Apr;70(2):403-416. Episode Summary: In this Journal Club edition of the Digital Pathology Podcast, we explore a wild paradox at the bleeding edge of diagnostic medicine. We examine a 2026 paper on artificial intelligence in oral and maxillofacial pathology that reveals a fascinating reality: while highly advanced AI models can ...

Send us Fan Mail Paper Discussed in this Episode: Assessing interstitial fibrosis and tubular atrophy in kidney biopsies artificial intelligence versus humans. Farris AB, Zukić D, Solez K. Current Opinion in Nephrology and Hypertension. March 16, 2026. Episode Summary: In this journal club deep dive on the Digital Pathology Podcast, we explore the intense debate over quantifying chronic kidney disease progression. We unpack a fresh 2026 study comparing artificial intelligence to human patholo...

Send us Fan Mail Paper Discussed in this Episode: Clarifying validation terminologies in healthcare. Amanda Dy, Sandra M. Buetow, Andrew J. Bredemeyer, et al. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02471-2. Episode Summary: In this deep dive, we unpack the silent chaos surrounding a single, universally used word in healthcare innovation: "validation". Exploring a 2026 paper by the Pathology Innovation Collaborative Community (PIcc), we uncover how differing definitions of ...

Send us Fan Mail Paper Discussed in this Episode: Deep learning-based histopathological classification and subclassification of benign and malignant salivary gland tumors. Weber A, Schuster D, Heyer J, Becker C, Burkhardt V, Werner M, Spörlein A, Bronsert P, Schulz T. European Archives of Oto-Rhino-Laryngology 2026. Episode Summary: In this journal club deep dive of the Digital Pathology Podcast, we explore a chaotic microscopic landscape to see if artificial intelligence can master one of th...

Send us Fan Mail Paper Discussed in this Episode: A confidence-based, artificial intelligence pathology model for diagnosis of intrahepatic cholangiocarcinoma. Chang, Jay, Calderaro, et al. Annals of Oncology 2026. DOI: 10.1016/j.annonc.2026.02.018. Episode Summary: In this journal club deep dive, we tackle one of the most frustrating diagnostic puzzles in liver cancer: differentiating primary intrahepatic cholangiocarcinoma (ICCA) from metastatic liver cancers. We examine a groundbreaking 20...

Send us Fan Mail Artificial Intelligence in Healthcare: From Diagnosis to Rehabilitation. Witek K, Nowocien M, Gerlach J, et al. Cureus 2026 Jan 25;18(1):e102286. Episode Summary: In this journal club deep dive on the Digital Pathology Podcast, we completely bypass the venture capital hype and science fiction narratives to look strictly at the hard clinical evidence surrounding artificial intelligence in medicine. We examine a monumental 2026 narrative review synthesizing a full decade's wort...

Send us Fan Mail Paper Discussed in this Episode: Reporting checklist for foundation and large language models in medical research (REFINE): an international consensus guideline. Mese I, Akinci D’Antonoli T, Bluethgen C, et al. Diagn Interv Radiol 2026. Episode Summary: In this special journal club edition of the digital pathology podcast, we tackle a massive structural problem in medical imaging and AI: the rapid adoption of foundation models and large language models (LLMs) that are complet...

Send us Fan Mail Paper Discussed in this AI Journal Club: "Transforming Gastric Biopsy Diagnostics: Integrating Omics Technologies and Artificial Intelligence" by Nasar Alwahaibi, published in the journal Biomedicines. Episode Summary: In this episode, we explore how traditional gastric biopsies are getting a massive, sci-fi-level upgrade. For over a century, diagnostic practice has relied heavily on visual pattern recognition via histomorphology—essentially looking at stained tissue und...

Send us Fan Mail Paper Discussed in this AI Journal Club: From Image-Guided Surgery to Computer-Assisted Real-Time Diagnosis with Hyperspectral and Multispectral Imaging: A Systematic Review in Gynecologic Oncology. Innocenzi C, Pavone M, Seeliger B, et al. Diagnostics 2026. Episode Summary: In this journal club deep dive, we explore a groundbreaking 2026 systematic review that challenges the traditional intraoperative frozen section. We examine how hyperspectral and multispectral imaging are...

Send us Fan Mail Paper Discussed in this AI Journal Club: Masry ME, Gnyawali S, Jacobson M, Xue Y, Sen C, Wachs J, Gordillo G. AutoMated Burn Diagnostic System for Healthcare (AMBUSH). Plast Reconstr Surg Glob Open. 2023 Oct 18;11(10 Suppl):128-129. doi: 10.1097/01.GOX.0000992564.42240.e3. PMCID: PMC10566867. Episode Summary: In this journal club deep dive, we tackle a clinical problem that has frustrated surgeons for decades: accurately diagnosing burn depth. We examine a groundbreakin...

Send us Fan Mail Paper Discussed in this AI Journal Club: Benchmarking large language model-based agent systems for clinical decision tasks. Liu, Y., Carrero, Z.I., Jiang, X. et al. npj Digit. Med. 2026. Episode Summary: In this episode, we dive into a comprehensive 2026 benchmarking study that tests whether the highly hyped "Agentic AI" systems are truly ready to revolutionize clinical decision-making. We pit baseline large language models (LLMs) against complex, multi-agent systems in a ser...

Send us Fan Mail Paper Discussed in this AI Journal Club: Wienholt, P., Caselitz, S., Siepmann, R. et al. Hallucination filtering in radiology vision-language models using discrete semantic entropy. Eur Radiol (2026). https://doi.org/10.1007/s00330-026-12384-z Episode Summary: In this deep dive, we strip away the marketing hype surrounding medical AI and confront the "black box" problem of Vision Language Models (VLMs) like GPT-4o. We examine a groundbreaking 2026 study published in...

Send us Fan Mail Paper Discussed in this AI Journal Club: Region-Based Segmentation of Lymph Node Metastases in Whole-Slide Images of Colorectal Cancer: A Pilot Clinical Study. Fayzullin A, Savelov N, Balkivskiy A, et al. Cancer Medicine 2026. Episode Summary: In this deep dive, we strip away the marketing gloss of AI as a mere time-saving tool and look at its true value in the lab: saving lives through relentless vigilance. We examine a 2026 study on colorectal cancer that deploys a two-stag...

Send us Fan Mail Paper Discussed in this AI Journal Club: Artificial Intelligence-Based Digital Image Analysis for Assessing Ki67, P53, and PHH3 Expression in Glioblastoma Multiforme. Devrim T, Erkilinc G, Tuncer SS. J Coll Physicians Surg Pak 2026; 36(02):153-157 Episode Summary: In this journal club deep dive, we step out of the theoretical future of AI and look at a direct, hard-data showdown between artificial intelligence and the human eye. We examine a groundbreaking 2026 stud...

Send us Fan Mail Paper Discussed in this AI Journal Club: Multimodal learning for scalable representation of high-dimensional medical data. Alsaafin A, Shafique A, Alfasly S, Kalari KR and Tizhoosh HR (2026). Front. Digit. Health 7:1709277. doi: 10.3389/fdgth.2025.1709277 Episode Overview In this episode, we tackle the infrastructure challenge in digital diagnostics: how do we efficiently store, search, and integrate the overwhelming amount of multimodal data generated by modern med...

Send us Fan Mail Source Material: This AI journal Club episode is based on the original article, "The patient matters: a roundtable discussion on pathology in the era of digitization and AI," authored by Frederik Deman, Heleen Lauwers, Glenn Broeckx, Roberto Salgado, and Amelie Dendooven (Virchows Archiv, 2026). In this episode, we dive deep into a critical yet often overlooked aspect of medical technology: the patient's perspective on the rapid integration of Digital Pathology (DP) and Artif...

Send us Fan Mail This AI Journal Club Episode is based on the following paper: Szylberg Ł, Durślewicz J, Chmura Ł, Rezner W, Bartczak A, Marszałek A. Guidelines for the adoption of digital pathology in clinical pathology units recommended by the polish society of pathologists. Diagn Pathol. 2026 Jan 30;21(1):13. doi: 10.1186/s13000-026-01762-2. PMID: 41618426; PMCID: PMC12874716. You can read it here: https://pubmed.ncbi.nlm.nih.gov/41618426/ You can view the YouTube version with captions her...

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 digital pathology model for renal pathology education, an open-source quality control tool for prostate b...

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 angles: how close is AI to being genuinely useful in real pathology practice — and what's still stand...

Send us Fan Mail You don't need a fancy scanner, a huge budget, or a computational background to get started in digital pathology. That's what I told the ACVP podcast — and I meant it. In this episode, I share my full digital pathology journey: from being completely intimidated by scanners during residency, to building a career that combines toxicologic pathology, image analysis, and remote work at a global CRO. If you're a resident, a trainee, or even a seasoned pathologist who hasn't fully ...

Send us Fan Mail How close is pathology AI to making decisions that matter in real workflows, real trials, and real patient care? In this episode of DigiPath Digest, I review five recent papers that approach that question from very different angles. We look at multimodal survival prediction in cervical cancer, pathology-driven response assessment in neoadjuvant immunotherapy for head and neck squamous cell carcinoma, AI-assisted Ki-67 scoring in pulmonary neuroendocrine neoplasms, automation ...

Send us Fan Mail Why do digital pathology projects get harder once the real workflow starts? In this USCAP 2026 conversation, I talk with Robert Moody from Hamamatsu and Jake Eden from Agilent about what the conference theme, MAKING CONNECTIONS, looks like in actual digital pathology implementation. This was not just a conversation about products. It was a conversation about workflow. We talked about why consistent staining matters before scanning, why strong partnerships need a shared vision...

Send us Fan Mail 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 fo...

Send us Fan Mail 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 qu...

Send us Fan Mail 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 fib...

Send us Fan Mail 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 th...

Send us Fan Mail 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 deploy...

Send us Fan Mail 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 expla...

Send us Fan Mail 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?”...

Send us Fan Mail 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] Com...

Send us Fan Mail 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...

Send us Fan Mail 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 ...

Send us Fan Mail 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 c...

Send us Fan Mail 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 pas...

Send us Fan Mail 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 ...

Send us Fan Mail 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 mind...

Send us Fan Mail 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,...

Send us Fan Mail What happens when AI becomes powerful enough to diagnose—not just one disease, but entire fields of medicine at once? In this episode of DigiPath Digest #33, I break down four new PubMed abstracts shaping the future of digital pathology, clinical AI integration, federated learning, and multidisciplinary cancer care. Across every study, one message is clear: AI is accelerating, but human oversight defines its safe adoption. Below are the full timestamps, key insights, and ref...

Send us Fan Mail Why does it take three years to deploy a digital pathology tool that only took three weeks to build? That’s the reality no one talks about—but every lab feels every time they deploy a new tool... In this episode, I sit down with Andrew Janowczyk, Assistant Professor at Emory University and one of the leading voices in computational pathology, to unpack the practical, messy, real-world truth behind deploying, validating, and accrediting digital pathology tools in the clinic. W...

Send us Fan Mail Why are billions of people still invisible in genomic research—and what does that mean for the future of precision medicine? In this episode, I sit down with Victor Angel Mosti, founder and CEO of Omica.Ai, for one of the most insightful conversations I’ve recorded about data equity and building ethical, community-centered AI. Victor shares not only his personal cancer story but also the staggering truth: Hispanic and Latino populations make up less than 1% of genomic dataset...

Send us Fan Mail How far can AI go in helping us diagnose disease—without losing the human judgment patients rely on? In this episode, I break down four studies shaping the future of digital pathology, oncology, and neurology. From spatial biology updates at SITC to voice-based Alzheimer’s detection, deep learning for sarcoma prognosis, and new guidelines for safe AI deployment, this week’s digest highlights where AI is making a real impact—and where caution still matters. Episode Highlights ...

Send us Fan Mail If your pathology reports and other data could talk, what would they say about the future of precision medicine? The truth is, most labs already have the data—they’re just not having a conversation with it. In this episode, I talk with Peter O’Toole, President and Chief Software Architect at mTuitive. We recorded live at Pathology Visions and are covering the power of structured data and how it’s redefining the future of pathology reporting, AI, and clinical decision support....

Send us Fan Mail Is your lab truly digitally ready—or just scanning slides? That’s the question I unpack in this live discussion from Day 2 of SITC’s 40th Anniversary Meeting, joined by David Anderson (Biocare Medical) and Don Ariyakumar (Hamamatsu Photonics). Together, we explore what digital readiness really means for multiplex immunofluorescence (mIF) and how to build reliable, reproducible workflows that scale from research to clinical settings. What We Discuss The Discovery Funnel ...

Send us Fan Mail Can spatial biology and multiplex immunofluorescence truly transform how we understand cancer? I went live from the Society for Immunotherapy of Cancer (SITC) 2025 — the 40th Anniversary Meeting to explore how spatial biology, multiplex IF, and digital pathology are coming together to redefine cancer diagnostics, research, and precision medicine. This session kicked off a weekend of cutting-edge discussions with leaders from Hamamatsu (Booth 415) and Biocare Medical (Booth 71...

Send us Fan Mail Can one AI system learn from every organ — and teach us something new about all of them? In this edition of DigiPath Digest #31, I explore how artificial intelligence is transforming pathology across multiple organ systems, revealing connections that help us diagnose faster, more consistently, and more accurately than ever before. From glomerulonephritis to hepatocellular carcinoma, AI is no longer confined to a single specialty — it’s becoming the connective tissue between t...

Send us Fan Mail If artificial intelligence can match—or even surpass—our diagnostic accuracy, what happens to the role of the pathologist? That’s the question I explore in this episode of DigiPath Digest #30, where I break down three fascinating papers showing how AI is changing the way we diagnose, classify, and predict outcomes in renal transplant biopsies, thyroid cytology, and gastrointestinal cancers. These studies don’t just prove AI’s potential—they reveal what it means for us, the hu...

Send us Fan Mail Why do some pathologists still hesitate to trust digital slides—even after the FDA says “yes”? Because accuracy in digital pathology isn’t just about pixels—it’s about precision, validation, and confidence. In this episode, I talk with Dr. Keith Wharton, MD, PhD, Global Medical Director at Roche Diagnostics, about how the Roche Digital Pathology DX system earned its FDA clearance for primary diagnosis—and what that means for the field. We explore the science and strategy behi...

Send us Fan Mail Live from Pathology Visions 2025 in San Diego, I share highlights from Day 2 of the world’s leading digital pathology conference, where experts explored how AI, empathy, and training are shaping the next generation of pathologists. This episode captures the shift from technology as a tool to technology as a bridge — helping us connect with patients in more meaningful ways. What I Talk About 1️⃣ From Pixels to Patients We’ve built the infrastructure; now it’s about applying i...

Send us Fan Mail Live from Pathology Visions 2025 in beautiful San Diego, I sat down with Imogen Fitt from Signify Research to explore how AI, digital pathology, and interoperability are transforming the way we diagnose cancer and deliver patient care. The conference theme, “From Pixels to Patients,” perfectly captures this year’s shift — from theoretical discussions about AI to real-world implementation and measurable outcomes. We’re no longer just asking “what can AI do?” — we’re seeing how...

Send us Fan Mail Will AI make doctors and specialists less skilled—or even replace them? That’s the question I explore in this episode of DigiPath Digest #29. As someone working where AI meets digital pathology, I’m both excited and cautious about how automation shapes our skills and professional identity. In this episode, I discuss two studies that ask tough questions about AI, expertise, and the future of medicine. What I Talk About: 1️⃣ Endoscopist Deskilling After AI Exposure (Lancet, 2...

Send us Fan Mail What if climbing the digital pathology “mountain” isn’t about reaching the summit alone—but knowing where base camp is, and who you bring with you? In this episode, I take you inside the Digital Diagnostic Summit in Park City, hosted by Lumea, where fewer than 100 digital pathology leaders gathered to share their journeys, challenges, and solutions. From resilient metaphors of Everest climbs to practical strategies for workflow ownership, clinical trials, and AI-powered...

Send us Fan Mail What if up to 35% of the diagnostic color data on your pathology slides never reaches your eyes—just because of your monitor? In this episode, sponsored by Barco, I sit down with Dr. Monika Lamba Saini (ADC Therapeutics) and Tom Kimpe (Barco) to uncover why color calibration in digital pathology isn’t optional anymore—it’s critical for diagnosis, efficiency, and AI readiness. Highlights: [00:03:42] Monika’s path from CROs to biopharma and why color consistency matters in cli...

Send us Fan Mail 7 Counterintuitive Secrets from NCCN’s 2025 AI in Cancer Care Summit When the National Comprehensive Cancer Network (NCCN) gathers healthcare leaders, people listen. I attended the 2025 Policy Summit on the evolving AI landscape in cancer care—and walked away with insights that were raw, practical, and surprisingly hopeful. Instead of hype or overpromising, cancer care leaders shared honest strategies for implementing AI responsibly and effectively. In this episode, I break d...

Send us Fan Mail What if the way we quantify pathology is more guesswork than science? In this episode of DigiPath Digest, I take you through the latest research where AI is not just supporting but challenging traditional methods of image analysis in neuropathology, nephrology, hematology, and cytology. From Boston brain banks to Mayo Clinic kidney models, we look at how advanced AI compares to human vision—and where it already outperforms us. Episode Highlights: [00:02:49] Neuropathology ima...

Send us Fan Mail What if the AI tools we trust for cancer diagnosis are not always correct? This episode of DigiPath Digest takes on the uncomfortable but critical question: can AI “lie” to us—and how do we verify its performance before adopting it in clinical practice? Highlights: [00:02:00] Foundation models in action: Deployment of a fine-tuned pathology foundation model for EGFR biomarker detection in lung cancer—reducing the need for rapid molecular tests by 43%.[00:08:41] Bone marrow AI...

Send us Fan Mail What if AI could predict cancer outcomes better than traditional methods—and at a fraction of the cost? In this episode, I explore how multimodal AI is reshaping lung and prostate cancer predictions and why integration challenges still stand in the way. Episode Highlights with Timestamps: [00:02:57] Agentic AI in toxicologic pathology – what it is and how it could orchestrate workflows.[00:05:40] Grandium desktop scanners – making histology studies more accessible and efficie...

Send us Fan Mail “AI in Pathology Isn’t Coming — It’s Already Here. Are You Ready?” From confusion to clarity — that’s what this episode is all about. I sat down with Drs. Liron Pantanowitz, Hooman Rashidi, and Matthew Hanna to dissect one of the most important and comprehensive AI-in-pathology resources ever created: the 7-part Modern Pathology series from UPMC’s Computational Pathology & AI Center of Excellence (CPAiCE). This isn’t just another opinion piece — it's your complete guide t...

Send us Fan Mail Can AI Grade Cancer Better Than Us? The Truth About T-Cell Imaging, Biomarkers & Digital Pathology Disruption You think Saturday mornings are for coffee? Try diving into bone marrow morphology, organ donor kidney biopsies, and AI-driven metastasis detection at sunrise. That’s how I do it—and you’re invited to join. Welcome to another data-packed episode of DigiPath Digest, where we explore the latest frontier in digital pathology and AI. This time, I reviewed some of the...

Send us Fan Mail Breast Epithelium Segmentation, TILs in Esophageal Cancer, Liver Fibrosis & Immunoscore If you still think visual quantification is “good enough” in pathology, think again. In this 27th episode of DigiPath Digest, I break down four abstracts that show how AI is shifting our diagnostic landscape—from breast cancer segmentation to fibrosis assessment, and all the way to spatial immunology and the evolving immunoscore. If you’re still relying on manual scoring, static stagi...