Digital Pathology Podcast

• 169: AI Across Organ Systems: Kidney, Liver, Colon, Bladder, and Beyond

  Send us a textCan 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 them.What’s Inside:1️⃣ AI for Bladder Cancer Classification We begin with a multicenter study validating AI models for urothelial neoplasm classification using over 12,000 whole-slide images. Both CNNs and transformer models achieved high accuracy (AUC 0.983, F1 score 0.9). I discuss why the F1 score matters — and what it tells us about model balance between sensitivity and specificity.2️⃣ AI in Colorectal Cancer Care Next, we explore multimodal AI — integrating histopathology, radiology, genomics, and blood markers to modernize colorectal cancer workflows. AI now helps detect adenomas, infer microsatellite instability (MSI) from H&E slides, and predict treatment outcomes. I highlight the critical need for external validation, interpretability, and governance as AI enters clinical use.3️⃣ AI for Glomerular Nephritis Diagnosis A deep learning model trained on over 100,000 kidney biopsy images identified four nephritis types — FSGS, IgA, MN, and MCD — with over 85% accuracy. This technology could ease workloads and improve turnaround time in renal pathology. Still, I share why AI support may feel both empowering and unsettling for many pathologists.4️⃣ AI in Liver Disease (MASLD & HCC) AI is advancing noninvasive fibrosis staging and risk prediction in liver pathology. From large consortia like NIMBLE and LITMUS to predictive models for HCC therapy response, AI is moving us closer to precision hepatology. I also discuss the challenge of translating these tools from research to regulatory approval.5️⃣ Lightweight AI for Domain Generalization Finally, we look at one of pathology AI’s biggest challenges: domain shift — when a model trained on one scanner or staining style performs poorly elsewhere. The new Histolite framework shows how lightweight, self-supervised models can generalize across data sources — trading some accuracy for reliability in real-world use.My TakeawayAcross every study, a single message stands out: AI isn’t replacing pathologists — it’s amplifying our vision. By connecting kidney, colon, liver, and bladder insights, AI is teaching us that medicine works best when it learns across boundaries.Episode HighlightsBladder cancer AI validation (06:41)Multimodal colorectal AI (12:38)Glomerular nephritis deep learning (19:29)AI in liver pathology (29:55)Domain shift & Histolite framework (38:17)Halloween wrap-up + SITC preview (46:18)Join me next time for updates from the SITC 2025 Conference, where I’ll be live at Booth 415 with Hamamatsu and Biocare, discussing how AI and spatial biology are converging to drive clinical utility.#DigitalPathology #AIinHealthcare #ComputationalPathology #CancerDiagnostics #LiverPathology #RenalPathology #FutureOfMedicine #DigiPathDigestSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

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• 168: Smarter Slides: How AI Is Reshaping Kidney, Thyroid & GI Pathology

  Send us a textIf 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 humans behind the microscope.Study 1 — Renal Transplant Biopsies: Precision in Every PixelA Japanese team examined how deep neural networks and large language models improve diagnostic consistency in renal transplant pathology.They highlighted how the Banff Digital Pathology Working Group is retraining AI models alongside updated Banff classifications—creating a dynamic feedback loop between human expertise and machine learning.In the U.S., over ten digital pathology systems are now FDA-cleared for primary diagnosis, showing that AI can support both accuracy and accountability. It’s not replacing us—it’s working with us.Study 2 — Thyroid Cytology: From Overdiagnosis to OptimizationAs someone who’s personally experienced thyroid cancer, this study hit close to home.Researchers in China developed AI-TFNA, a multimodal system that combines whole-slide images and BRAF mutation data from over 20,000 thyroid fine-needle aspirations across seven centers.The model achieved 93% accuracy, reducing unnecessary surgeries and improving clinical decisions. What’s especially impressive is Image Appearance Migration (IAM)—a technique that helps AI adapt across scanners and labs, ensuring reliable performance worldwide.Study 3 — GI Cancer: Prognosis ReimaginedAn international collaboration of over 2,400 patients introduced a Deep Learning Pathomics Signature (DLPS) that merges nuclear features, tumor microenvironment, and spatial single-cell data.This AI-driven model predicted patient survival and therapy response more accurately than traditional TNM staging—even identifying which patients are most likely to benefit from chemotherapy or immunotherapy.It’s precision medicine powered by pathology.Reflections:Each of these studies made me think about the balance between trust and technology.  We’ve reached a point where AI can truly enhance diagnostic precision—but it also challenges us to stay actively engaged, curious, and informed.Because the real risk isn’t that AI will outperform us—it’s that we’ll stop thinking critically once it does.That’s why collaboration between pathologists, data scientists, and industry innovators matters more than ever.AI isn’t replacing us—it’s redefining what excellence looks like in pathology.#DigitalPathology #AIinHealthcare #ComputationalPathology #RenalPathology #ThyroidCytology #CancerDiagnostics #DigiPathDigestSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

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• 167: Why Accuracy Matters in Digital Pathology Podcast with Keith Wharton, Jr.

  Send us a textWhy 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 behind whole slide imaging (WSI) validation, the challenges of feature recognition, the meaning of non-inferiority, and the future of interoperability and AI in diagnostic systems.If you’ve ever wondered what it takes to make a digital system clinically equivalent to the microscope—this episode is your roadmap.🔹 Highlights with Timestamps[00:00–02:30] What “primary diagnosis” really means—and how the human brain processes histopathology features.[02:30–06:00] How Roche achieved FDA clearance for its DP200 and DP600 scanners—and why it’s “clearance,” not “approval.”[06:00–11:00] Breaking down the Roche Digital Pathology DX System components: scanner, viewer (Navify DP), and monitor.[11:00–16:00] Understanding the pixel pathway—the heart of system validation.[16:00–19:00] How the FDA defines precision and accuracy in validation studies.[19:00–30:00] Inside the massive multi-year validation studies: design, washout periods, and thousands of slide reads.[30:00–33:00] The non-inferiority margin (−4%)—why it matters and how Roche exceeded the benchmark.[39:00–45:00] The surprising “nuclear groove” discovery and what it reveals about how pathologists adapt to digital.[1:10:00–1:13:00] Future-ready systems and FDA flexibility through predetermined change control plans (PCCP).[1:25:00–1:35:00] Keith’s reflection: bridging the gap between discovery and clinical impact, and why the future of digital pathology is brighter than ever.Resources from This EpisodeRoche Digital Pathology DX (DP200 & DP600) – FDA-cleared systems for primary diagnosisFDA Guidance (2016) – Technical performance standards for WSIAmerican Journal of Clinical Pathology – Paper in press on validation study designBook: Chasing the Invisible by Dr. Thomas GroganFrontiers Journal (2021) – Tissue Multiplex Analyte Detection in Anatomic Pathology (co-authored by Aleks and Keith)AJCP PaperAleks and Keith’s Paper - Frontiers | Tissue Multiplex Analyte Detection in Anatomic Pathology – Pathways to Clinical ImplementationKey Takeaways✅ FDA clearance requires rigorous demonstration of precision, accuracy, and statistical confidence.✅ Non-inferiority margins (typically −4%) define the threshold for clinical equivalence to microscopy.✅ Feature recognition in digital environments (like nuclear grooves) challenges perception and training.✅ Interoperability and predetermined change control plans (PCCP) may accelerate system evolution.✅ Digital pathology’s foundation is the pixel pathway—where scanner, viewer, and monitor all align.✅ The field’s future depends on bridging discovery and practice, guided by robust validation.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

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• 165: How AI Is Changing Cancer Diagnosis Insights from PathVision 2025

  Send us a textLive 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 it’s actually improving accuracy, reducing barriers, and connecting pathologists and labs worldwide.What We Discuss1️⃣ From Hype to Application This year, the buzz wasn’t about AI’s potential — it was about how it’s being used. We highlight case studies showing how digital tools are reducing diagnostic errors, improving collaboration, and even helping smaller labs digitize faster and more affordably.2️⃣ PathPresenter’s Expanding Role We dive into PathPresenter’s innovative model that gives users access to digital pathology at no initial cost, opening the door for over 75,000 professionals across 62 institutions. I share why I personally use PathPresenter for teaching and how it’s helping lower the barrier to entry for education, consultations, and patient care.3️⃣ New Scanning Technology and Accessibility We talk about compact scanners like Grundium’s four-slide scanner and new miniature models that make digitization possible even in smaller labs. The message is clear: you don’t need a massive system to start going digital.4️⃣ Collaboration and AI in Action Imogen shares updates from across Europe and Asia, including how hospitals are tackling storage, AI regulation, and workflow efficiency. We discuss emerging partnerships—Fujifilm, Voicebrook, Dolby, and others—that are making voice dictation, chat agents, and real-time AI insights part of the modern pathology cockpit.5️⃣ The Human Side of AI Adoption We also reflect on how digital pathology is changing careers and training. Younger pathologists expect digital tools as part of their workflow — and many won’t settle for less. We discuss how this new generation is driving adoption and pushing institutions to modernize.My ReflectionsI still remember when digital pathology felt intimidating — when only a few people were “allowed” to touch the scanner. But today, that’s changed completely.Now, we’re living in an era where AI and digital pathology are not optional — they’re essential. The technology has matured, and so has the mindset around it. What excites me most is seeing how collaboration and accessibility are becoming central to innovation.Key TakeawaysAI in pathology is moving from hype to practice — focused on improving patient outcomes.Accessibility matters: smaller, affordable scanners and open platforms are democratizing digital pathology.Collaboration between vendors, clinicians, and technologists is key to faster, smoother adoption.The next generation of pathologists expects — and demands — a digital-first workflow.Listen Now to Learn:How AI is reshaping cancer diagnosisThe tools driving real change in labs todayHow collaboration fuels digital transformation in pathologySupport the showGet the "Digital Pathology 101" FREE E-book and join us!

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• 164: What Happens to Human Expertise When AI Takes Over in Medicine

  Send us a textWill 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, 2025)A multicenter Polish study found that after frequent AI-assisted colonoscopy use, endoscopists’ adenoma detection rate dropped by ~6% when performing procedures without AI. It suggests overreliance on automation can subtly dull vigilance.It reminded me of how we depend on GPS instead of remembering routes—or how driving an automatic car changes focus. Could medicine be facing a similar shift?2️⃣ “Will My Expertise Be Devalued by Machines?” (Bangladesh, 2024)Healthcare professionals shared concerns about:Job security and evolving roles 💼Ethics, accountability, and trust ⚖️Losing the human touch ❤️The need for AI training and oversight 📚AI adoption isn’t just technical—it’s behavioral, cultural, and deeply human.My Take:I see AI as a partner, not a threat. I use it every day for research and content, but I never outsource judgment. AI can boost efficiency—but only if we stay curious, critical, and engaged.We can’t let convenience replace competence. AI should augment our expertise, not erode it.🌍 PathVision 2025 — Sept 5–7, 2025I’m also thrilled to share that I’ll be livestreaming PathVision 2025 from September 5–7, 2025, on LinkedIn and YouTube! 🎥This year’s conference is packed with innovations in AI, digital pathology, and cancer diagnostics. I’ll bring you live insights, interviews, and key takeaways from the sessions—so mark your calendars and tune in!🧩 Key TakeawaysContinuous AI use may lower independent performance.Professionals worry about trust, ethics, and losing skill.The goal isn’t to resist AI—but to use it critically and consciously.The best outcomes happen when AI and human expertise work together.🕒 Episode Highlights00:00–06:14 | Welcome & PathVision preview06:14–17:46 | AI deskilling question08:27–14:05 | Colonoscopy study results27:11–38:10 | Healthcare workers’ AI concerns43:59–51:05 | Reflections & responsible AI use51:05–52:55 | Closing thoughts + PathVision invite🧭 MentionedLancet Gastroenterology & Hepatology (2025): “Endoscopist deskilling risk after AI exposure”Bangladesh Study (2024): “Will my training be devalued by machines?”My Book: Digital Pathology 101 (Updated Edition Coming Soon)Event: PathVision 2025 – Sept 5–7, 2025 (Streaming Live!)Thanks for listening to DigiPath Digest #29! I hope it inspires you to think critically about how we can embrace AI without losing what makes us human.And don’t miss PathVision 2025 (Sept 5–7, 2025)—I’ll be streaming it live for three days of insights, innovation, and community. Let’s keep learning and leading the future of digital pathology togSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

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