Digital Pathology Podcast

• 176: Can AI Protect Patients? Forensics, Pathomics & Breast Cancer Insights

  Send us a textWhat 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 & HIGHLIGHTS0:00 — 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 LimitsBased 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 disciplines10:55 — Confocal Endomicroscopy + AI for Pancreatic CystsResearchers trained a deep model on 291,045 endomicroscopy frames to detect papillary and vascular structures in IPMNs:70% faster review timeMore consistent structure identificationA step toward scalable “optical biopsy” workflows 📌 Source: IPMN / confocal endomicroscopy AI abstract16:40 — Federated Learning in Computational PathologyA comprehensive review of FL for:Tissue segmentationWhole-slide image classificationClinical 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 review22:15 — The Lucerne Toolbox 3: A Digital Health Roadmap for Early Breast CancerA 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 screeningPersonalized screening strategiesDigital knowledge databasesAI-driven treatment optimizationDigitally 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 AcceptableWe 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 barriersConfocal Endomicroscopy + AI for IPMN — hiSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

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• 175: Deploying Digital Pathology Tools - Challenges and Insights with Dr. Andrew Janowczyk

  Send us a textWhy 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[00:00–04:20] Why multidisciplinary collaboration is the non-negotiable cornerstone of clinical digital pathology deployment[04:20–08:30] Real-world insight: The H. pylori detection tool and how it surfaces “top 20” likely regions for pathologist review[08:30–12:50] The painful truth: Algorithms take weeks to build—but years to deploy, validate, and accredit[12:50–17:40] Why curated research datasets fail in the real world (and how to fix it with unbiased data collection)[17:40–23:00] Algorithmic hardening: turning fragile research code into production-ready clinical software[23:00–28:10] Why every hospital is a snowflake: no standard workflows, no copy-paste deployments[28:10–33:00] The 12 validation and accreditation roles every lab needs to define (EP, DE, QE, IT, etc.)[33:00–38:15] Validation vs. accreditation—what they are, how they differ, and when each matters[38:15–43:40] Version locking, drift prevention, and why monitoring is as important as deployment[43:40–48:55] Deskilling concerns: how AI changes perception and what pathologists need before adoption[48:55–55:00] Usability testing: why naive users reveal the truth about your UI[55:00–61:00] Scaling to dozens of algorithms: bottlenecks, documentation, and the future of clinical digital pathology and AI workflowsResources From This EpisodeJanowczyk & Ferrari: Guide to Deploying Clinical Digital Pathology Tools (discussed)Sectra Image Management System (IMS)Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study - PubMedDigital Pathology 101 (Aleksandra Zuraw)Key TakeawaysAlgorithm creation is the easy part—deployment is the mountain.Clinical algorithms require multidisciplinary ownership across 12 institutional roles.Real-world data is messy—and that’s exactly why algorithms must be trained on it.No two hospitals are alike; every deployment requires local adaptation.Usability matters as much as accuracy—naive users expose real workflow constraints.PathoSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

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• 174: How Do We Fix the Bias in Biomedical AI Podcast with Victor CEO and Founder of Omica.Ai

  Send us a textWhy 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[00:00–01:40] Personal cancer experiences and diagnostic uncertainty[01:40–06:50] Victor’s medical journey across Mexico and the U.S.[06:50–11:42] The digitization gap: empathy vs. tech[11:42–16:43] The “coffee diversity” metaphor for genomic diversity[16:43–19:34] Funding disparities & the biotech cold-start problem[19:34–25:44] Digital pathology as a gateway to precision medicine[25:44–31:44] Avoiding “parachute science” and building community-first research[31:44–36:05] The Nagoya Protocol and benefit-sharing[36:05–41:47] Omica.Ai’s work, goals, and clinical-embedded approach[41:47–49:36] Creating future-proof, embedded biobanks[49:36–53:35] Blockchain for transparency and patient trust[53:35–54:39] Victor’s call to action: collaborate, include, and stay humanResources from This EpisodeOmica.Ai – Community-driven precision medicine platformNagoya Protocol – Framework for equitable biological useKey InsightsCancer is personal—even for experts<1% representation of Latino genomes threatens clinical accuracyDigital pathology + AI can leapfrog infrastructure gapsEthical biobanking requires trust, transparency, and local benefitAvoiding “parachute science” is essentialGenetic diversity drives discovery—but only if we capture itBlockchain + dynamic consent = future of patient-centered dataSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

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• 173: AI and the Human Touch: Patient Safety, Prognosis & Voice Biomarkers

  Send us a textHow 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 Highlights1️⃣ 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.TakeawaysmIF is maturing quickly but needs standardized, end-to-end workflows.Digital neuropathology is ready for broader Alzheimer’s research.Safe AI requires multidisciplinary collaboration and rigorous validation.Voice biomarkers may become powerful tools for early cognitive assessment.Deep learning can refine prognosis and reveal hidden tumor patterns.ResourcesHamamatsu (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 showGet the "Digital Pathology 101" FREE E-book and join us!

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• 172: Why Structured Reporting Is the Future of Pathology | mTuitive on Workflow, Data & Compliance with Peter O'Toole

  Send us a textIf 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[00:00–05:40] Data as the new currency in pathology — Why structured data is the foundation for clinical, research, and trial insights.[05:40–10:30] AI & Large Language Models (LLMs) — What AI can (and can’t) do when your data isn’t structured.[10:30–19:25] AI workflow integration & voice recognition — How AI and structured reporting work together inside the LIS and IMS.[19:25–25:27] Overcoming resistance — Why pathologists initially resisted structured reports and how perceptions are shifting globally.[25:27–29:53] Decision support & beyond cancer — Expanding structured data to liver, skin, and even mental health pathology.[29:53–34:15] Collaboration as the catalyst — How partnerships create seamless ecosystems for pathology data.[34:15–37:03] Demo: Synoptic reporting in action — Real-time staging, automation, and compliance made easy.Resources from this EpisodemTuitive website: https://mtuitive.comCAP Synoptic Reporting Protocols – Standardized templates for structured pathology reports.Pathology Visions Conference 2025 – Event where this discussion took place.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 showGet the "Digital Pathology 101" FREE E-book and join us!

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