Digital Pathology Podcast

Send a text 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 that help frame this challenge and point toward the next steps for trustworthy AI in healthcare. You will hear about the role of prospective validation, real-world effectiveness, transparent reporting standards, and multimodal data integration as recurring themes across these studies. Key Highlights 00:00 – Introduction What do we do when AI detects signals that humans cannot see? The core challenge is verifying those outputs before trusting them in clinical decision making. 03:32 – AI Across the Healthcare Continuum A narrative review shows AI achieving clinician-level performance in well-defined imaging tasks, including digital pathology. But most evidence comes from retrospective or controlled environments, and prospective validation remains limited. 08:34 – Multi-Omics and AI in Gastric Biopsy Diagnostics Morphology alone cannot fully capture molecular heterogeneity or predict disease progression. Integrating genomics, proteomics, metabolomics, and other omics with AI is shifting gastric pathology toward data-driven precision gastroenterology. 13:38 – Hyperspectral Imaging for Real-Time Surgical Guidance Spectral imaging can analyze tissue composition during surgery without staining, freezing, or contact with the tissue. Studies show promising sensitivity for detecting malignancy and supporting intraoperative decision making. 17:20 – REFINE Reporting Guideline for Foundation Models and LLMs An international consensus guideline introduces a 44-item reporting checklist to standardize how AI studies are described. The goal is transparent, reproducible, and comparable research in medical AI. 22:35 – Big Takeaway AI should be viewed as clinical decision support, not a replacement for clinicians. Real-world validation, ethical governance, and reproducible research standards will determine how these tools enter pathology workflows. References (Articles Discussed) Artificial Intelligence in Healthcare: From Diagnosis to Rehabilitation https://pubmed.ncbi.nlm.nih.gov/41755929/ Transforming Gastric Biopsy Diagnostics: Integrating Omics Technologies and Artificial Intelligence https://pubmed.ncbi.nlm.nih.gov/41751306/ From Image-Guided Surgery to Computer-Assisted Real-Time Diagnosis with Hyperspectral and Multispectral Imaging https://pubmed.ncbi.nlm.nih.gov/41750768/ REFINE Reporting Guideline for Foundation and Large Language Models in Medical Research https://pubmed.ncbi.nlm.nih.gov/41762555/ If you enjoy staying current with digital pathology and AI research, this episode will help you connect the dots between promising algorithms and practical clinical adoption. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send a text 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 deployment. Clear. Practical. Evidence-based. ⏱ Topics & Timestamps [00:02] Introduction Weekly journal club on digital pathology and artificial intelligence. [05:13] Hallucination Filtering in Radiology Using Discrete Semantic Entropy to detect hallucination-prone responses in Vision Language Models. Accuracy improved from 51.7 percent to 76.3 percent after filtering high-entropy answers. [15:04] Artificial Intelligence in Pathology Training Supervised use during residency. Balancing artificial intelligence adoption with preservation of morphological analysis and critical thinking. [20:12] Colorectal Cancer Lymph Node Detection Two-stage classification and segmentation model in Whole Slide Imaging. Recall 1.0. Specificity 0.935. Dice coefficient 0.818. Artificial intelligence as a second opinion. [25:04] Burn Depth Prediction with Artificial Intelligence Tissue Doppler Elastography and Harmonic B-mode ultrasound combined with artificial intelligence. 90 to 95 percent accuracy in human subjects. [31:20] Agent-Based Large Language Model Systems OpenManus and Manus evaluated in clinical simulations. Up to 60.3 percent accuracy. High computational cost. 89.9 percent of hallucinations filtered by safeguards. [40:08] Patient Access to Pathology Images Why viewing pathology slides can empower patients and improve communication. Resources https://pubmed.ncbi.nlm.nih.gov/41720937/https://pubmed.ncbi.nlm.nih.gov/41720644/https://pubmed.ncbi.nlm.nih.gov/41716065/https://pubmed.ncbi.nlm.nih.gov/41709317/https://pubmed.ncbi.nlm.nih.gov/41708802/Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send a text 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 Ingied 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 explainable AI can significantly reduce that uncertainty. Ingred shares her journey from applied mathematics and computer science into pathology, the skepticism she faced in 2008, and why CADESS.AI chose not to “optimize H&E,” but instead developed a Picrosirius red + hematoxylin stain designed specifically for computational pathology. We discuss how grading at the gland and cellular level improves reproducibility, why explainability matters for trust, and what it really takes to build both stain and software as a single diagnostic workflow. This conversation challenges long-held assumptions—and asks whether improving data quality should come before building smarter algorithms. Highlights: Resources from This Episode CADESS.AINCCN prostate cancer risk stratification guidelinesSupport the show Get the "Digital Pathology 101" FREE E-book and join us!

Send a text 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?”, this episode is for you. Because building the model is often the easy part — deployment is where things get complex. This guide breaks the process into four practical phases every lab can follow: 1️⃣ Pre-Development – Define your clinical need, project scope, and validation plan before writing a single line of code. 2️⃣ Development – Build and integrate the algorithm in a production-ready environment. 3️⃣ Validation & Hardening – Turn your research code into a reliable, secure, and compliant clinical tool. 4️⃣ Production & Monitoring – Keep the tool validated and performing consistently over time. We also discuss what makes qualification, validation, and accreditation different — and why that order really matters. You’ll hear about the multidisciplinary team behind these deployments, especially the deployment engineer (DE) — the technical linchpin who turns AI research into clinical reality. I share the story of HUG’s H. pylori detection tool, which cut diagnostic time by 26% while maintaining a 0% false negative rate. The team’s secret? Careful planning, quality control, and continuous user feedback — not just great code. Other highlights include: If you’re working on digital or computational pathology tools — or just want to understand how AI safely moves from research to routine diagnostics — this episode will give you a roadmap grounded in real experience. 🎧 Listen now to learn how to move from algorithm to accreditation, step by step. And if you’re just getting started in digital pathology, I’d love to give you my free eBook, Digital Pathology One-on-One: All You Need to Know to Start and Continue Your Digital Pathology Journey. You’ll find the link to download it in the show notes. See you in the episode! Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send a text 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] Community & updates Digital Pathology 101 free PDF, upcoming patient-focused book, and global attendance. [04:07] AI-based image analysis in glioblastoma AI showed strong consistency with pathologists when quantifying Ki-67, P53, and PHH3. Significant biological correlations (Ki-67 ↔ PHH3, PHH3 ↔ P53) were detected by AI — not by manual assessment. Takeaway: computational quantification improves precision. [09:28] Real-world digital workflow + AI in prostate cancer (France) AI-pathologist concordance: • 93.2% (high probability cancer detection) • 99.0% (low probability slides) Gleason concordance: 76.6% 10% failure rate due to pre-analytical artifacts. Takeaway: infrastructure and sample quality still matter. [15:58] Multimodal AI (MARBIX framework) Combines whole slide images + immunogenomic data in a shared latent space using binary “monograms.” Performance in lung cancer: 85–89% vs 69–76% unimodal models. Takeaway: integrated data improves case retrieval and similarity reasoning. [22:13] AI-powered paper summary subscription introduced Structured summaries for busy professionals who want more than abstracts. [26:17] Patient roundtable on AI in pathology (Belgium) Patients expect: • Better accuracy • Faster turnaround • Stronger collaboration Trust is high when: • Algorithms use diverse datasets • Pathologists retain final responsibility Clinical validity mattered more than full algorithm transparency. Privacy concerns focused more on insurer misuse than cloud transfer. Key Takeaways If you’re working in digital pathology, computational pathology, or precision oncology, this episode connects evidence, implementation, and patient perspective. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send a text 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 monitor calibration, visually lossless compression (20:1), scalable storage, pathologist-driven validation, and what “non-inferiority” truly means. Digital pathology is not just a change of medium. It’s an operational shift. Episode Highlights [00:02] Community & growth 1,600+ new newsletter subscribers, 10,000+ Facebook members, and free Digital Pathology 101 book access. [07:20] The 4 pillars of adoption Hardware fidelity · Data integrity · Clinical validation · Future integration. [08:30] Hardware requirements 40x equivalent scanning (≤0.26 μm/px), 4K monitors, >300 cd/m² luminance, 10-bit color depth. [12:00] Workflow & throughput 200–300 slides/day per scanner, automated focus control, urgent case prioritization. [17:25] Storage & archiving ~1 GB per slide. Active archive (6–24 months). Long-term retention (10–20 years). GDPR compliance & TLS encryption. [23:09] Validation philosophy Pathologist-centered validation. Two phases: • Familiarization (~20 retrospective cases) • Dual review with discrepancy tracking Goal: digital must be non-inferior to glass. [29:03] AI in digital pathology AI supports quantification (Ki-67, HER2, ER/PR, PD-L1), tumor detection, and future multimodal predictions — but pathologists remain central. [33:26] Intraoperative telepathology <5-minute scan-to-view time. Minimum 100 Mbps upload. Redundancy and safety protocols required. [34:50] Can digital cameras replace scanners? Hybrid workflows exist. Regulatory compliance still applies. [38:19] Adoption checklist summary Certified scanners (CE-IVD/FDA), calibrated monitors, scalable storage, phased validation, and documented QC. Key Takeaways Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send us a text 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 not about hype. It’s about standards, validation, data integrity, and clinical translation—the factors that decide whether AI tools stay in research or reach patient care. Episode Highlights 01:2108:0518:1023:0132:1429:42Key Takeaways Resources Mentioned Digital Pathology 101 (free PDF & audiobook)Publication Links: a. https://pubmed.ncbi.nlm.nih.gov/41618426/ b. https://pubmed.ncbi.nlm.nih.gov/41616271/ c. https://pubmed.ncbi.nlm.nih.gov/41610818/ d. https://pubmed.ncbi.nlm.nih.gov/41595938/ e. https://pubmed.ncbi.nlm.nih.gov/41590351/ Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send us a text 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 cost-effectiveness modeling in oncology, this session connects innovation with real-world clinical impact. Across all discussions, one theme is clear: progress depends not just on performance, but on integration, validation, interpretability, and trust. HIGHLIGHTS: 00:00–05:30 | Welcome & 2026 Outlook New year reflections, global community check-in, and upcoming Digital Pathology Place initiatives. 05:30–16:00 | LLMs for Clinical Phenotyping How GPT-4 and NLP automate phenotyping from free-text EHR notes in Crohn’s disease, reducing manual chart review while matching expert performance. 16:00–23:30 | AI Screening for Fabry Nephropathy A computational pathology pipeline identifies foamy podocytes on renal biopsies and introduces a quantitative Zebra score to support nephropathologists. 23:30–29:30 | Is AI Cost-Effective in Oncology? A Markov model evaluates AI-based response prediction in locally advanced rectal cancer, highlighting when AI delivers value—and when it does not. 29:30–38:30 | LLM-Guided Arbitration in Multimodal AI A multi-expert deep learning framework uses large language models to resolve disagreement between AI models, improving transparency and robustness. 38:30–44:30 | Real-World AI & Cautionary Notes Ambient clinical scribing in practice, AI hallucinated citations, and why guardrails remain essential. KEY TAKEAWAYS • LLMs can extract meaningful clinical phenotypes from narrative notes at scale • AI can support rare disease diagnosis without replacing expert judgment • Economic value matters as much as technical performance • Explainability and arbitration are becoming critical in multimodal AI systems • Human oversight remains central to responsible adoption Resources & References https://www.digitalpathologyplace.comDigital Pathology 101Automating clinical phenotyping using natural language processingZebra bodies recognition by artificial intelligence (ZEBRA): a computational tool for Fabry nephropathyCost-effectiveness analysis of artificial intelligence (AI) for response prediction of neoadjuvant radio(chemo)therapy in locally advanced rectal cancer (LARC) in the NetherlandsA multi-expert deep learning framework with LLM-guided arbitration for multimodal histopathology predictionSupport the show Get the "Digital Pathology 101" FREE E-book and join us!

Send us a text 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 past hype and pilots and started asking harder questions about validation, workflow integration, regulation, and trust. I also share what I believe matters most as we move into 2026: building real-world evidence, upskilling pathologists, and focusing on tools that genuinely support patient care rather than distract from it. This episode is for anyone navigating change in pathology and wondering where to invest their time, energy, and curiosity next. Episode Highlights: Key Takeaways: Resources Mentioned Digital Pathology Place2025 CONFERENCE Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send us a text 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 Big Picture 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 what it really takes to build a 3-million-slide repository across 44 partners, why GDPR and data-sharing agreements delayed progress by 18 months, and how sustainability, trust, and collaboration are just as critical as technology. This conversation is about the unglamorous—but essential—work of building infrastructure that will shape pathology AI for decades. ⏱️ Highlights with Timestamps 📚 Resources from This Episode Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send us a text 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 mindset shift our field must embrace to move forward. Digital pathology is no longer “early adoption.” It’s becoming essential infrastructure. And yet the biggest barrier isn’t scanners or algorithms—it’s the knowledge and confidence needed to use them well. Key Highlights & Timestamps 0:00 — Setting the Stage from London An overview of the forces that shaped digital pathology in 2025: workflow integration, clinical readiness, and the move from theory to operational reality. 1:45 — Leica’s Expanded Portfolio & FDA-Cleared Collaborations A look at Leica’s updated scanner lineup and co-developed, FDA-cleared solutions with Indicollabs. These launches reflect a broader industry trend toward highly specialized, clinically validated digital tools designed for end-to-end workflows. 4:12 — The Acceleration of Companion Diagnostics From Artera’s de novo–approved prostate prognostic test to AstraZeneca’s TROP2 scoring efforts, 2025 pushed computational pathology directly into therapeutic decision-making. 6:20 — Why Workflow Integration Became the Theme of 2025 Partnerships like BioCare + Hamamatsu + Visgen and Zeiss + MindPeak show where the field is heading: full-stack solutions, not isolated tools. Labs want interoperability, reliability, and simplified digital workflows. 9:10 — Adoption Challenges: ROI, Education & AI Uncertainty We explore the realities slowing digital transformation: – ROI is real, but requires workflow change – AI anxiety persists among clinicians and patients – Education is still the strongest driver of adoption 12:00 — 2025’s Innovation Highlights Breakthroughs shaping the next phase of digital pathology include: – emerging agentic AI platforms – voice-enabled image management systems – improved multiplexing technologies like Hamamatsu’s Moxiplex 15:40 — The Growing Intersection of Pathology & Genomics AI models predicting genomic alterations from H&E images gained traction, especially for cases with minimal tissue. Tempus acquiring Paige signals the deepening connection between digital workflows and molecular data. 18:30 — What 2026 Will Require Priorities for the coming year include: – building agentic AI solutions capable of real workflow orchestration – strengthening validation and QC – sharing real-world deployment case studies – expanding training and hands-on learning RESOURCES: 1. The Lucerne Toolbox 3: digital health and artificial intelligence to optimise the patient journey in early breast cancer-a multidisciplinary consensus 2. Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send us a text 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, I show why knowledge—not infrastructure—is what truly opens doors. Highlights and Key Themes 0:00 – The Limiting Belief I open with the core misconception I hear from pathologists worldwide: “I need a scanner before I can start.” I explain why hesitation, not lack of equipment, is the real barrier—and why waiting for perfect conditions keeps many people stuck. 2:24 – My Early Digital Pathology Story I describe my residency in 2013, when a single scanner was “off limits” to trainees. Faced with a research project requiring consistent cell counting, I improvised using a microscope camera and Microsoft Paint. It wasn’t sophisticated, but it was digital, consistent, and reproducible. This experience taught me a foundational lesson: if you can measure something, measure it; don’t rely on visual estimation. 7:01 – How This Led to My First Digital Pathology Job That basic Paint-and-dots project became my gateway to working at Definiens (now part of AstraZeneca). I wasn’t hired for computational expertise; I was hired because I understood tissue, biology, and the value of quantifying what we see. Working alongside image analysis scientists showed me the exponential power of combining tissue knowledge with computational tools. 10:03 – Dr. Tala Zafar’s Story I share the inspiring journey of Dr. Tala Zafar from Karachi, Pakistan, who began with no access to scanners and only a microscope camera. During COVID shutdowns, she taught herself the foundations of digital pathology, joined global organizations, conducted a nationwide survey, and contacted AI vendors for access to platforms. After many rejections, one vendor offered a trial account. In just six weeks, she completed three AI projects using microscope camera images—each one published in a peer-reviewed journal. Her story highlights a universal truth: starting with curiosity and persistence matters far more than having perfect tools. 14:14 – Two Paths After a Conference I explain the difference between the “forgetting loop” and the “learning path.” Many attendees leave inspired but slip back into routine. Others commit to one consistent learning habit—journal clubs, vendor webinars, DigiPath Digest sessions—and return a year later with clarity, confidence, and momentum. These individuals become the people others seek out for guidance in digital pathology. 18:04 – Where to Begin You don’t need a scanner or an institutional budget to start. What you need is structured knowledge. I introduce my book, Digital Pathology One on One, and encourage listeners to choose one learning habit to build on after the episode. The only wrong choice is choosing nothing. 19:06 – Final Message Knowledge drives adoption, not infrastructure. Scanners, AI tools, and computational platforms already exist. What’s missing are people who understand how to interpret tissue digitally, collaborate with computational teams, and bridge biology with technology. You have Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send us a text 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 referenced research to help you explore each topic more deeply. TIMESTAMPS & HIGHLIGHTS 0:00 — Welcome & Opening Question How far can AI safely scale across medicine—and where must humans stay in control? 4:10 — AI in Forensic Medicine: Accuracy Meets Ethical Limits Based on a systematic review, we discuss: AI advances in personal identification, pathology, toxicology, radiology, anthropology. Benefits: reduced diagnostic error, faster case resolution. Challenges: data diversity gaps, limited validation, lack of ethical frameworks. 📌 Source: PubMed abstract on AI in forensic disciplines 10:55 — Confocal Endomicroscopy + AI for Pancreatic Cysts Researchers trained a deep model on 291,045 endomicroscopy frames to detect papillary and vascular structures in IPMNs: 70% faster review time More consistent structure identification A step toward scalable “optical biopsy” workflows 📌 Source: IPMN / confocal endomicroscopy AI abstract 16:40 — Federated Learning in Computational Pathology A comprehensive review of FL for: Tissue segmentation Whole-slide image classification Clinical outcome prediction Key takeaway: FL can match or outperform centralized training—without sharing patient data—yet still struggles with heterogeneity, interoperability, and standardization. 📌 Source: Federated learning review 22:15 — The Lucerne Toolbox 3: A Digital Health Roadmap for Early Breast Cancer A global consortium of 112 experts identified 15 high-impact knowledge gaps and proposed 13 trial designs to integrate AI across early breast cancer care: AI-based mammography screening Personalized screening strategies Digital knowledge databases AI-driven treatment optimization Digitally delivered follow-up & supportive care 📌 Source: The Lucerne Toolbox 3 (Lancet Oncology) 28:50 — Big Picture: AI Expands What’s Possible—but Humans Define What’s Acceptable We close with the essential takeaway echoed across all four publications: AI is getting smarter, faster, and more integrated—but clinical responsibility, validation, transparency, and multidisciplinary alignment remain irreplaceable. STUDIES DISCUSSED AI in Forensics — systematic review examining applications & ethical barriers Confocal Endomicroscopy + AI for IPMN — hiSupport the show Get the "Digital Pathology 101" FREE E-book and join us!

Send us a text 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. We walk through Andrew’s experience building and implementing an H. pylori detection algorithm at Geneva University Hospital—a project that exposed every hidden challenge in the transition from research to a clinical-grade tool. From algorithmic hardening, multidisciplinary roles, usability studies, and ISO 15189 accreditation, to the constant tug-of-war between research ambition and clinical reality… this conversation is a roadmap for anyone building digital tools that actually need to work in practice. Episode Highlights non-negotiableyears Resources From This Episode Janowczyk & Ferrari: Guide to Deploying Clinical Digital Pathology ToolsSectra Image Management System (IMS)Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study - PubMedDigital Pathology 101 (Aleksandra Zuraw) Key Takeaways Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send us a text 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 datasets. This underrepresentation isn’t just a data gap—it’s a clinical risk. We dive into disparities between healthcare systems, the promise of digital pathology as a low-cost entry point, the dangers of “parachute science,” and how Victor is building a living, ethical, transparent biobank through Omica. AI—built for true precision medicine rooted in community trust. Highlights with Timestamps Resources from This Episode Omica.AiNagoya Protocol Key Insights Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send us a text 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 1️⃣ SITC Trends & Spatial Biology (00:00 → 07:40) I share key updates from SITC 2025, including the growing role of multiplex immunofluorescence (mIF) and the need for integrated staining-to-scanning workflows. I also preview new educational content and upcoming podcast guests in global AI research. 2️⃣ Digital Neuropathology & Alzheimer’s (07:40 → 13:01) A major review confirms that digital neuropathology is now robust enough for large-scale Alzheimer’s studies—opening doors for computational tools to link histology with cognition. 3️⃣ Patient Safety in AI (13:01 → 19:56) An Italian review underscores the foundations of trustworthy AI: dataset quality, transparency, oversight, and continuous validation. I discuss why “patient-centered AI” must remain our standard. 4️⃣ Voice Biomarkers for Cognitive Decline (19:56 → 26:43) AI models analyzing short speech recordings are showing high accuracy for early Alzheimer’s detection. This could make future screening simple, noninvasive, and more accessible. 5️⃣ Deep Learning for Sarcoma Prognosis (34:06 → 35:59) A multi-instance CNN outperforms FNCLCC grading by identifying prognostic patterns in tumor center and periphery regions, offering new insights into soft-tissue sarcoma biology. Takeaways Resources Hamamatsu (MoxiePlex) • Biocare Medical (ONCORE Pro X) • SITC Programs • Recent publications on AI biomarkers and computational pathology. Thanks for listening—and for being part of this growing digital pathology community. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send us a text 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. We explore how structured reporting evolved from checklists to intelligence, why data hygiene and workflow integration matter more than AI buzzwords, and how collaboration across companies like mTuitive is helping labs turn their reports into clinically actionable data. Highlights with Timestamps Data as the new currency in pathologyAI & Large Language Models (LLMs)AI workflow integration & voice recognitionOvercoming resistanceDecision support & beyond cancerCollaboration as the catalystDemo: Synoptic reporting in action Resources from this Episode https://mtuitive.comCAP Synoptic Reporting ProtocolsPathology Visions Conference 2025 Key Takeaways ✅ Structured reporting transforms pathology data from static text into actionable intelligence. ✅ AI and LLMs complement structured data—but can’t replace its clinical readiness. ✅ Clean data in = clean data out—data hygiene defines AI reliability and efficiency. ✅ Workflow integration and user-friendly design drive real-world adoption. ✅ Structured data unlocks clinical trials access, research potential, and decision support tools. ✅ Collaboration is key to building the connected ecosystem pathology needs. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send us a text 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 I open by situating mIF within the broader discovery funnel: researchers begin with hundreds of biomarkers, narrowing down to focused 4–10 marker panels where true clinical utility begins. But this only works if the lab is digitally prepared from the start—from slide prep to data capture. Defining Digital Readiness David Anderson reframes digital readiness as everything that happens before the scanner turns on: AI and image analysis toolsThe Pre-Analytical Foundation Don Ariyakumar emphasizes that scanning can’t fix variability. If staining or section quality isn’t standardized, digitization simply amplifies inconsistencies. True readiness starts at the bench, not the monitor. Integration Across Vendors We also talk about how interoperability between stainers, scanners, and spatial biology software is becoming essential. A disconnected workflow—mixing manual, unaligned steps—adds variables that no algorithm can fully normalize. Lessons from IHC’s Evolution The team draws parallels between multiplex IF today and IHC’s early days: once complex, now routine. Multiplex IF promises even richer tumor microenvironment insights, but only if standardization and automation catch up to the technology. Beyond the Funnel I revisit the “funnel” metaphor in a new light—arguing that as precision medicine grows, the bottom of the funnel broadens, not narrows. That means more tailored, smaller panels rather than one-size-fits-all assays, and a growing need for efficient, reproducible digital workflows. Key Takeaways Biocare’s ONCORE Pro XHamamatsu’s MoxiePlexa cornerstone of precision pathologyResources Mentioned 🔹 Biocare Medical (Booth 717) — ONCORE Pro X™ open slide stainer automating mIF, IHC, FISH, and ISH protocols. 🌐 biocare.net 🔹 Hamamatsu Photonics (Booth 415) — MoxiePlex™ multispectral imaging platform for high-plex spatial analysis. 🌐 hamamatsu.com 🔹 Society for Immunotherapy of Cancer (SITC) — 40th Anniversary Meeting information and programs. 🌐 sitcancer.org Timestamp Highlights 00:00 — Welcome from SITC Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send us a text 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 717) — two companies helping laboratories around the world embrace digital transformation and spatial imaging in oncology. 🧠 Episode Highlights & Key Moments 0:00 — Introduction I set the stage live from SITC 2025, explaining the goal of this series: to connect the science of multiplex imaging and spatial analysis with the practical needs of today’s cancer pathologists and researchers. ~1:00 — What Is Multiplex Immunofluorescence (IF)? I explain how multiplex IF enables simultaneous detection of multiple biomarkers and immune cell types within a single tumor sample — giving us an unprecedented look at the tumor microenvironment and how cells interact. ~2:30 — The Spatial Biology Revolution We talk about spatial biology as the “next frontier” beyond traditional histopathology — visualizing not just what is on the slide, but where it happens. ~5:00 — Digital Pathology & AI Readiness I discuss the importance of digital pathology systems for slide digitization and how AI-powered software is now helping identify biomarkers, quantify expression, and accelerate immunotherapy research. ~7:30 — Featured Booths at SITC 2025 Hamamatsu (Booth 415):Biocare Medical (Booth 717):ONCORE Pro Xmultiplex IF, IHC, FISH, and ISH~9:00 — Real-World Impact We walk through clinical case examples where multiplex IF data guides immunotherapy decisions — helping clinicians stratify patients and tailor treatments more precisely. ~12:00 — Getting Started I share practical advice for researchers ready to adopt spatial biology or digital pathology, from workflow design to validation and staff training. ~15:00 — Audience Q&A Live questions from the audience on implementation, data integration, and scaling multiplex workflows across research and clinical environments. ~20:00 — Future Directions We look ahead to how machine learning and spatial data integration will shape the next decade of immuno-oncology, including new SITC workshops on AI-driven tissue profiling. ~24:00 — Wrap-Up & Takeaways Key message: spatial biology is not just a trend — it’s the next layer of precision medicine. I invite everyone to visit Hamamatsu (Booth 415) and Biocare (Booth 717) and to stay tuned for the next livestream focused on multiplex IF in clinical settings. Resources Mentioned 🔹 Hamamatsu Photonics (Booth 415) High-performance digital slide scanners and imaging systems. 🌐 hamamatsu.com 🔹 Biocare Medical (Booth 717) ONCORE Pro X — Open sli Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send us a text 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 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 Takeaway Across 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 Highlights 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 #DigiPathDigest Support the show Get the "Digital Pathology 101" FREE E-book and join us!