Digital Pathology Podcast

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 intellig...

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 re...

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 trad...

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 hap...

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...

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 f...

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" proble...

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 tac...

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 t...

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, hallu...

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%...

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 patholog...

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 conne...

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...

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...

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...

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 actual...

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 h...

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&amp...

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 comp...

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 dia...

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 t...

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 high...

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 per...

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...