Podcasts about digital pathology

Send us a textWhat 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 image analysis (Boston VA & BU) – Why traditional semiquantitative scoring often fails, and how AI-based density quantification reveals more subtle pathology in CTE.[00:13:16] Chronic kidney changes with AI (Mayo Clinic, Cambridge, Emory, Geneva) – A 20-class AI model trained on 20,500 annotations, showing how multiclass segmentation outperforms human guesswork in renal pathology.[00:21:09] Digital hematology review (University of Pennsylvania) – Current hurdles in AI for blood and bone marrow evaluation: regulatory oversight, data standardization, and resistance to change.[00:25:52] AI in cytology review (Journal of Cytopathology) – From BD FocalPoint to deep learning: two decades of digital cytology, stagnation, and why adoption still lags despite proven benefits.[00:32:09] Neuropathology goes digital – Where digital neuropathology is already routine (Ohio State, Mayo Clinic, Leeds, Granada) and why this specialty is crucial for pushing adoption.[00:34:19] Personal note – Why I believe learning, sharing, and experimenting with AI tools now will shape the way we practice pathology tomorrow.Resources from this EpisodeComparison of quantitative strategies in neuropathologic image analysis – Boston VA / BU Brain Bank study.Multiclass AI model for chronic kidney changes – Mayo Clinic, Cambridge, Emory, Georgia Tech, Geneva collaboration.Review: Digital hematology in the AI era – International Journal of Laboratory Hematology.Review: AI and machine learning in cytology – Journal of the American Society of Cytopathology.Digital Pathology 101 (by me, Dr. Aleksandra Zuraw) – Free PDF & Amazon print edition.Pathology AI Makeover Course – Practical training for AI in pathology workflows.Support the showBecome a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

Send us a textWhat 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 misclassifications: Why automated digital morphology still struggles with consistency across leukemia and lymphoma cases.[00:14:45] Lossy DICOM conversion: How file format changes can subtly—but significantly—affect AI model performance.[00:21:45] Federated tumor segmentation challenge: Coordinating 32 international institutions to benchmark healthcare AI fairly across diverse datasets.[00:27:47] AI in gynecologic cytology: Reviewing AI-driven Pap smear screening—promise, limitations, and why rigorous validation remains essential.[00:32:27] Takeaway: Trust but verify—AI tools must be validated before they can support or replace clinical decisions.Resources from this EpisodeNature Medicine – Fine-tuned pathology foundation model for lung cancer EGFR biomarker detection.Scientific Reports (Germany) – Study on how DICOM conversion impacts AI performance in digital pathology.Federated Tumor Segmentation Challenge – Benchmarking AI across 32 global institutions.Acta Cytologica – Review on AI in gynecologic cytology and Pap smear screening.Support the showBecome a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

Send us a textWhat 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 efficient.[00:08:03] Clover framework – a cost-effective multimodal model combining vision + language for pathology.[00:13:40] NSCLC study (Beijing Chest Hospital) – AI predicts progression-free and overall survival with high accuracy.[00:17:58] Prostate cancer prognostic model (Cleveland Clinic & US partners) – validating AI-enabled Pathomic PRA test.[00:23:35] Thyroid neoplasm classification – challenges for AI in distinguishing overlapping histopathological features.[00:34:49] Real-world Belgium case study – AI integration into prostate biopsy workflow reduced IHC testing and turnaround time.[00:41:03] Lessons learned – adoption hurdles, system integration, and why change management is essential for successful digital transformation.Resources from this EpisodeWorld Tumor Registry – A global open-access repository for histopathology images: World Tumor RegistryBeijing Chest Hospital NSCLC AI Prognostic Study – Prognosis prediction using multimodal models.Cleveland Clinic Pathomic PRA Study – Independent validation of AI-enabled prostate cancer risk assessment.Grandium Scanners – Compact desktop scanners for histology slides: Grandium.aiSupport the showBecome a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

Send us a text“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 to understanding, implementing, and navigating AI in pathology with real-world insights and a global lens.Together, we discuss:Why pathologists and computer scientists are often lost in translationHow AI bias, regulation, and ethics are being addressed — globallyWhat it really takes to operationalize AI in patient care todayIf you've ever asked, “Where do I even start with AI in pathology?” — this is your answer.

Send us a textCan AI Grade Cancer Better Than Us? The Truth About T-Cell Imaging, Biomarkers & Digital Pathology DisruptionYou 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 most exciting recent abstracts spanning cancer grading, T-cell quantification, and AI agents in oncology decision-making.These studies aren't just fascinating—they're redefining what's possible in diagnostics, especially in under-resourced areas where digital pathology can create game-changing access and efficiency.

Send us a textAI Pathology & Genomics: A New Benchmark for Predicting Gene MutationsIf you still think visual quantification is “good enough” in pathology, think again. In this 27th episode of DigiPath Digest, I break down four transformative 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 staging systems, or single-marker immunohistochemistry, this episode will challenge you to look deeper—literally and algorithmically.

Send us a textIf our visual scoring is still based on gut feeling, how do we scale precision? In this week's DigiPath Digest, I explored four new AI-focused papers that could reshape how we diagnose prostate, bladder, gastroesophageal, and endocrine cancers.From automated IHC scoring to predicting urethral recurrence post-cystectomy, these studies highlight the growing value—and responsibility—of integrating AI into our pathology workflows.And yes, I also reveal where to get my histology-inspired earrings

Send us a textIf we don't learn to work with LLMs now, we might end up competing with them.

Send us a textAI in Pathology: ML-Ops and the Future of DiagnosticsWhat if the most advanced AI models we're building today are doomed to die in the machine learning graveyard?

Send us a textCan We Ever Eliminate Bias in AI for Pathology?Every time we think we've trained a “neutral” algorithm, we discover our own fingerprints all over it. Our biases. Unconscious. Systemic. Data-driven. And if we ignore them, AI won't just fail—it will fail patients.Welcome back, my digital pathology trailblazers! In this sixth episode of our 7-part AI in Pathology series, we tackle one of the most uncomfortable yet necessary conversations: Ethics and Bias in AI and Machine Learning. These are not abstract philosophical concerns—they are critical decisions that affect diagnostic accuracy, fairness, and patient safety.We lean heavily on the brilliant work co-authored by Matthew Hanna, Liam Pantanowitz, and Hooman Rashidi, published in Modern Pathology, which you can read here: Ethics and Bias in AI for Pathology.Let's explore where bias creeps in, how we can mitigate it, and what it means to be a responsible data steward in digital pathology.⏱️ Highlights & Timestamps[00:00:00] Welcome back! Kicking off from Pennsylvania at 6:00 AM and reflecting on USCAP highlights, upcoming podcasts, and a pivotal lawsuit on LDTs. [00:03:00] Defining today's topic: Bias in AI—why it matters, and how pathologists are key players in shaping ethical, trustworthy algorithms. [00:05:00] Who are the “data stewards”? A new term you need to own. We explore the role of healthcare professionals in AI development and deployment. [00:07:00] Ethical principles decoded—autonomy, beneficence, non-maleficence, justice, and accountability—and how they translate to AI and ML. [00:11:00] From voting rights to data rights: A surprising analogy from my U.S. citizenship interview about the evolution of fairness. [00:12:00] 12 types of bias explained—from data bias to feedback loops, representation to confirmation bias—with real pathology examples. [00:22:00] Temporal bias and transfer bias: Why yesterday's data may not apply to today's patients. [00:26:00] Walkthrough of the AI lifecycle and how bias seeps in at every stage—from research to regulatory approval. [00:29:00] Clinical trials & guidelines: Learn the difference between STARD-AI, TRIPOD-AI, QUADAS-AI, and CONSORT-AI. [00:33:00] Visual case study: Gleason score distribution by region shows how biased training data leads to misdiagnosis. [00:37:00] Real-world mitigation: I spotlight Digital Diagnostics Foundation and Big Picture Consortium as proactive models for bias reduction. [00:41:00] Why explainability and introspection are more than buzzwords—they are our tools for ensuring accountability. [00:44:00] FAIR data principles—Findability, Accessibility, Interoperability, and Reusability—and why annotations often fall short. [00:48:00] Practical steps: How to build better algorithms with built-in fairness, bias detectors, and responsible data sharing.

Send us a textThe Most Overlooked Risk in AI for Pathology? It's Not What You Think…Welcome, my trailblazing digital pathologists! In this episode, I dive headfirst into the regulatory maze of Artificial Intelligence (AI) in pathology, covering global frameworks, safety risks, ethics, and the future of software as a medical device. While regulation might not be the flashiest part of AI, ignoring it could cost us innovation—or worse, patient safety.We're on Part 5 of our 7-part AI in Pathology series, and this one's vital for anyone developing, using, or simply curious about AI and machine learning tools in healthcare.If you thought regulation was boring, think again—it's what separates a helpful algorithm from a dangerous black box.

Send us a textIn this episode sponsored by Epredia, Dr. Anil Parwani explores the transformative journey of digital pathology from basic slide scanning to AI-driven diagnostics. He shares real-world implementation experiences and demonstrates how these technologies are addressing critical challenges in pathology practice.Pathology faces increasing demands amid workforce shortages and knowledge explosionDigital pathology provides standardization, objectivity, and automation beyond glass slidesOhio State University has scanned 4.2 million slides representing nearly 500,000 cases since 2016Current AI applications include biomarker quantification, rare event detection, and tumor classificationIntegration challenges remain the primary barrier to seamless adoption of AI toolsFuture technologies include virtual staining, 3D pathology, and large language model integrationArtificial intelligence remains task-oriented while real intelligence is context-aware and knowledge-basedEach institution must navigate their own "digital pathology chasm" based on specific needsDigital tools will augment pathologists' capabilities rather than replace human expertiseThe technology marketplace offers solutions for every stage of the digital transformation journeyThis Episode's ResourcesEpredia Digital Pathology WebsiteThis Episode on YouTubeComing soon!Support the showBecome a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

Send us a textYou might be using AI models in pathology without even knowing if they're giving you reliable results. Let that sink in for a second—because today, we're fixing that.In this episode, I walk you through the real statistics that power—and sometimes fail—AI in digital pathology. It's episode 4 of our AI series, and we're demystifying the metrics behind both generative and non-generative AI. Why does this matter? Because accuracy isn't enough. And not every model metric tells you the whole story.If you've ever been impressed by a model's "99% accuracy," you need to hear why that might actually be a red flag. I share personal stories (yes, including my early days in Germany when I didn't even know what a "training set" was), and we break down confusing metrics like perplexity, SSIM, FID, and BLEU scores—so you can truly understand what your models are doing and how to evaluate them correctly.Together, we'll uncover how model evaluation works for:Predictive Analytics (non-generative AI)Generative AI (text/image generating models)Regression vs. Classification use casesWhy confusion matrix metrics like sensitivity and specificity still matter—and when they don't.Whether you're a pathologist, a scientist, or someone leading a digital transformation team—you need this knowledge to avoid misleading data, flawed models, and missed opportunities.

Send us a textWhat if I told you the biggest AI breakthroughs in pathology aren't coming from ChatGPT or generative tools—but from the quiet power of predictive analytics and machine learning?In this episode, I explore the non-generative side of artificial intelligence in pathology. These are the tools that detect tumors, segment tissue, classify images, and make predictions—without generating a single word.It's the third chapter in our guided AI series, and this time we focus on the models you're more likely to use in real-world diagnostics. You'll hear about object detection, segmentation, anomaly detection, and how these models are built using supervised and unsupervised learning—plus the pros and cons of different annotation strategies.We'll also cover why no one model fits all, and how combining simple tools like decision trees with more complex neural networks is often the key to building reliable, usable AI in pathology.Whether you're training your first model, selecting an algorithm for rare disease detection, or just want to understand what “unsupervised clustering” means—you'll find something useful here.

Send us a text❗️Is synthetic data trustworthy enough to train AI for patient care? It just might be—and that's what both excites and terrifies me. ❗️Hey trailblazers! In this episode of the Digital Pathology Podcast, I take you through the second part of our AI in Pathology series—this time, we're focusing on generative AI and how it's revolutionizing diagnostics, education, and workflow in our field.From synthetic H&E slides that could pass for real to multimodal agents that can read your histology images and chat with you about them—yes, really—this is where digital pathology meets the “bleeding edge” of AI development.We'll also look at real use cases, a synthetic biobank you can trust, and the biases, hallucinations, and ethical minefields that come along for the ride.

Send us a textGenerative vs. Non-Generative AI in Pathology: Why the Difference MattersIf we don't start defining what kind of AI we're talking about, we risk letting buzzwords replace real science.

Send us a textWill FDA rules disrupt the way we diagnose diseases? In this episode, I break down a seismic shift in lab medicine: a federal court has vacated the FDA's controversial rule classifying lab-developed tests (LDTs) as medical devices. This change carries serious implications for innovation, digital pathology, AI-based diagnostics, and small labs across the U.S.

Send us a textYou think going digital in pathology just means buying a scanner? Think again. In this episode sponsored by Epredia, I sat down with Ryan Davis, Director of Global Business Strategy at Epredia, to talk about what it really takes to implement digital pathology—and why modularity, cytology support, and AI integration are changing the game. Whether you're starting your digital journey or scaling up with advanced tech, there's something in this conversation for you.

Send us a textIn this episode, I talk with Tiffany Chen, MD, and Ben Cahoon from Techcyte about Fusion, their new digital pathology platform. Fusion integrates clinical and anatomic pathology workflows, AI algorithms, and electronic health records—all into one streamlined experience.We explore how Fusion simplifies case management, improves diagnostic accuracy, and brings AI-powered pathology into routine practice. Plus, we discuss the importance of open standards, partnerships with Mayo Clinic, and why flexible integration is key for healthcare innovation.If you're passionate about digital pathology, AI, and advancing patient care, this is a conversation you don't want to miss!✨ Key Highlights- Introduction of Techcyte's Fusion platform: bridging clinical and anatomic pathology workflows- How Fusion integrates AI, EHRs, and LIS systems using open standards (FHIR, DICOM, HL7)- Collaborations with Mayo Clinic and BD for scalable global deployment- AI marketplace support: Fusion enables the integration of internal, partner, and institutional AI models- Impact of AI on cytology workflows and pathology screening- Flexibility for lab-driven or PACS-driven workflows- Future plans: Subspecialty-focused AI enhancements and smart synoptic reporting integration- The importance of interoperability and data standardization for healthcare AIThis Episode's Resources:Techcyte Fusion Platform: https://techcyte.com/fusion/ Support the showBecome a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

Send us a textWhy do so many digital pathology tools stall before they ever reach patients? In this USCAP 2025 special sponsored by Muse Microscopy, I talk with Esther Abels, founder of SolarisRTC, regulatory strategist, and the force behind the first FDA-cleared whole slide imaging system.We break down what startups and established companies must do from day one to succeedbin getting their devices through the FDA. Hint: regulatory strategy isn't a final step—it's your starting line.

Send us a textCan you still call it “digital transformation” if you're scanning slides and still tethered to glass?This special episode, recorded at USCAP and sponsored by MUSE Microscopy, features Dr. Robert Osamura from Japan. We explore how digital pathology is being implemented across Japanese hospitals, how regulations shape adoption, and where AI and tools like MUSE could fit in a geographically complex healthcare system. We also discuss the real-world utility of direct-to-digital tools for intraoperative diagnostics and how AI is changing confidence, not replacing pathologists.

Send us a textWhy are so many pathologists still afraid of going digital? In this USCAP special episode sponsored by Muse Microscopy, I talk with Dr. Sarah Dry, Chair of Pathology and Laboratory Medicine at UCLA, about real-world adoption, AI fear, and how change is best managed when it's people-led.From her early digital research lab in 2007 to pioneering innovative workflows at UCLA today, Dr. Dry knows how direct-to-digital imaging and AI can enhance, not replace, our work.

Send us a textWhy is digital pathology progressing faster in some parts of the world than others? In this international episode sponsored by Muse Microscopy, I sit down with Junya Fukuoka and Norman Zerbe—presidents of the Asian and European Societies of Digital Pathology—to unpack how cultural, regulatory, and infrastructural forces are shaping progress differently across continents.From direct-to-digital tissue imaging considered an alternative to frozen sections in Asia, to legal hurdles in Europe, we discuss what's advancing adoption—and what's still holding it back.

Send us a textAt the last  Pathology Visions 2024, I sat down with Imogen Fitt of Signify Research and Nick Best from Pathology News for a candid, energetic recap of what's really shaping the future of digital pathology.We discuss how two pathologists drove digital pathology adoption in their lab,  the reality behind radiology partnerships, the cautionary tale of AI burnout, and how the Technology Buyer's Guide helps pathologists navigate endless scanner options. From standardization and DICOM to staffing crises, remote workflows, and even Meta glasses—we covered it all.

Send us a text“It used to take 40 minutes, now it takes 15” - this is what Dr. Alae Kawam said about her AI-powered prostate biopsy evaluation workflow.  In this energizing episode of the Digital Pathology Podcast recorded at PathVisions 2024, Dr. Alae Kawam joins me to reflect on where pathology is headed—from AI-assisted prostate diagnostics to direct-to-digital imaging and beyond. Together, we unpack what's working, what still feels clunky, and why standardization, staffing flexibility, and smarter AI are critical to the next phase of pathology adoption.

Send us a textWhat does it take to build a digital pathology movement across the most diverse region on Earth?In this episode of the Digital Pathology Podcast, I'm joined by Dr. Junya Fukuoka, practicing pathologist, educator, and founder of the Asian Society of Digital Pathology (ASDP). From Japan to India, Saudi Arabia to South Korea, Asia's digital pathology adoption is growing rapidly—and Dr. Fukuoka is helping lead the charge.We talk about why digital access, multilingual support, and patient advocacy are central to pathology adoption across Asia's diverse regions. We also explore what it means to “skip” a step in tech, and why static images and direct-to-digital imaging may be Asia's most powerful tools.

Send us a textIn this episode, I'm joined by Dr. Hamid Tizhoosh, professor of biomedical informatics at the Mayo Clinic, to unravel what's truly holding back AI in healthcare, especially pathology. From the myths of general-purpose foundation models to the missing link of data availability, this conversation explores the technical and ethical realities of deploying AI that's accurate, consistent, lean, fast, and robust.

Send us a textIn this episode of the Digital Pathology Podcast, I sit down with Dr. Yuri Nikiforov, founder of the World Tumor Registry, to explore how this global, open-access whole slide image platform contributes to cancer diagnostics, education, and research.We talk about how the registry allows pathologists, researchers, and patients to view curated whole-slide images from around the world, starting with thyroid tumors and expanding into other cancers like breast and lung. Learn how AI, molecular diagnostics, and editorial curation come together to build a truly global pathology tool that's free for everyone, forever.

Send us a textIn this episode of the Digital Pathology Podcast, I explore the ethical and bias considerations in AI and machine learning through the lens of pathology. This is part six of our special seven-part series based on the landmark Modern Pathology review co-authored by the UPMC group, including Matthew Hanna, Liam Pantanowitz, and Hooman Rashidi.From data bias and algorithmic bias to labeling, sampling, and representation issues, I break down where biases in AI can arise—and what we, as medical data stewards, must do to recognize, mitigate, and avoid them.

This week's Expert is Jeff McIntyre, Vice President, Liver Programs at the Global Liver Institute. His major topic is how recent high-level FDA job cuts might affect MASH drug and diagnostics development. He also shares reactions to FibroSIGHT, HistoIndex's new digital pathology service for clinical practice. Highlight: Recent job cuts at the FDA will produce chaos in government and slow response to any emerging crises.Second Highlight: Patient self-advocacy becomes even more important in this environment.The conversation takes place on April 1, which lends context to Jeff's opening comment about the rate and nature of change in Washington, DC. He and Roger quickly focus on high-level job cuts at the FDA. Jeff believes that the clearest outcome from these changes is that the government will be less able to respond promptly and in a medically appropriate manner to future health crises. Jeff agrees with former FDA Commissioner Rob Califf's comment that the FDA as we know it "is dead," and that we have little idea what the future holds. A slower-moving, more chaotic government with a Secretary of HHS who minimizes pharmacotherapies for alternative therapies presents a challenge for all SLD patients. Jeff states that patients need to become more vigilant self-advocates (even more than they are today). He also identifies patient advocacy organizations like GLI as a place patients can go to seek the guidance and support they need from patient advocates. Finally, the conversation turns to discuss FibroSIGHT. Jeff describes FibroSIGHT as "exactly where we should be and should not be at the same time," a technology that takes a significant step forward in understanding and patient support, but one that ties us to biopsy as a standard for clinical care. Jeff and Roger agree this issue will play out over the coming years. 

This week's newsmaker, Yukti Choudhury, Director of Clinical Development at HistoIndex, joins Roger Green to discuss FibroSIGHT, a new HistoIndex service that allows clinicians to use HistoIndex's Second Harmonic Generation (SHG) technology and analytics to determine specific CRN fibrosis level for patients with inconclusive NIT results.  One reason FibroSIGHT is worthy of attention: This is the first time an in-depth analysis of clinical trial biopsy results is being placed at the service of clinical treatment. Another reason: Yukti states that demand for this technique could equal 163,000 cases this year, rising to one million by 2028. The interview starts with Yukti sharing information on her own academic and commercial background and how she came to this role. She describes FibroSIGHT, a service that will provide a highly accurate CRN fibrosis level for patients whose NIT results suggest no clear or consistent finding. Yukti provides practical cues on ordering the test and its reimbursement. Roger shares his long-standing respect for SHG and the clarity it produces. He notes the economic benefit of determining whether a patient has F2 fibrosis, which is indicated for pharmacotherapy, vs.F1, which is not indicated. He sees clear benefit in this analysis. Roger goes on to express concern that any option requiring more biopsies will reduce the number of patients treated, particularly if having this tool encourages payers to require a biopsy as a prerequisite to treatment. He asks whether, over time, HistoIndex might be able to develop a companion analytic to improve these estimates without requiring biopsy. 

CME credits: 0.50 Valid until: 04-04-2026 Claim your CME credit at https://reachmd.com/programs/cme/revolutionizing-diagnostic-precision-ai-driven-approaches-in-digital-pathology-and-her2-expression/29878/ With the availability of HER2-directed therapies, it's important to accurately identify patients who would benefit from these therapies, particularly in breast cancer, where we now have a spectrum of HER2 positivity. Advances in technology have augmented the role of digital pathology (DP) and artificial intelligence (AI) in oncologic pathology. This activity demonstrates how DP/AI can be used for more accurate biomarker assessment and explores the impact this may have on patient outcomes.=

Send us a textUSCAP 2025 Daily Update – Day 4 with Dr. Aleksandra ZurawIt's the final day of USCAP 2025, and in this episode of the Digital Pathology Podcast, I'm sharing personal moments, spontaneous tech wins (and fails), and meaningful conversations with some of the most forward-thinking voices in digital pathology.From running around with mics and misplaced tripods to interviewing Dr. Dry and Dr. Ozumura, this episode captures both the spirit of innovation and the real-world challenges of advancing digital workflows—especially in environments where regulations still lag behind.

Send us a textUSCAP 2025 Daily Update – Day 3 Recap with Dr. Aleksandra ZurawIn this episode of the Digital Pathology Podcast, I bring you Day 3 insights live from USCAP 2025—from moderating the MUSE panel on slide-free imaging to exploring regulatory strategies, tech innovations, and collaborations across the digital pathology community.Get an inside look at how direct-to-digital pathology is transforming workflows, how companies like Techcyte are streamlining AI applications, and why regulatory strategy is as crucial as your scanning tech.

Send us a textUSCAP 2025 Daily Update – Day 1 Highlights from Dr. Aleksandra ZurawWelcome to the first live daily update from USCAP 2025, recorded straight from the conference floor by Dr. Aleksandra Zuraw, your host at Digital Pathology Place. In this episode, Aleks shares behind-the-scenes moments, exciting vendor previews, and key updates as the world's largest pathology meeting kicks off.

Send us a textUSCAP 2025 Daily Update – Day 2 with Dr. Aleksandra ZurawWelcome back to the Digital Pathology Podcast live from USCAP 2025! On Day 2, I dive into the momentum building across the conference—covering major trends, tech insights, and how digital pathology is no longer just a topic, but a critical tool used to deliver sessions and share knowledge.From vendor-driven presentations to real-world applications of AI and slide-free technology, this episode explores how digital workflows are being integrated into education, diagnostics, and collaboration on a global scale.

Send us a textIn this episode of the Digital Pathology Podcast, I sit down with Matthew Nuñez, CEO of MUSE Microscopy, to discuss the groundbreaking advancements in direct-to-digital imaging in pathology. Traditional pathology workflows rely on glass slides, formalin fixation, and time-consuming processing steps. But what if we could skip the slide entirely and go straight to digital?

Send us a textHow Can Digital Pathology Workflows Stay Compliant and Efficient?In this episode of the Digital Pathology Podcast, I sit down with Scott Randall, Senior Application Specialist at Hamamatsu (Hamamatsu NanoZoomer), and Amanda Coble, Senior Director of Product for Proscia (Proscia's Website), to discuss the critical role of compliance, interoperability, and efficiency in digital pathology workflows.

Today my guest is Jason Maloney, VP of Customer Experience at Proscia.  What we discuss with Jason: His background in customer-facing roles across various company sizes. Being drawn to Proscia by the opportunity to impact lives through digital pathology. Customer Experience (CX) encompasses professional services, technical support, and customer success, focusing on the post-sale experience. Many companies in the industry focus on product development without adequately addressing customer needs and experiences. Jason applies insights from various industries, emphasizing the importance of treating customers as humans and understanding their workflows. Proscia's CX strategies have led to reduced time to go live with software and improved overall customer satisfaction. Proscia fosters a continuous feedback loop between customers and internal teams, enhancing product development and customer support. Proscia's CX team helps pathologists and researchers integrate AI into their workflows, providing expertise and support. The future focus will be on developing trusted advisor relationships with customers, enhancing customer success initiatives, and deepening problem-solving capabilities.   Links for this episode: Pathologists' Assistant Shadowing Network on LinkedIn Health Podcast Network  LabVine Learning Dress A Med scrubs Digital Pathology Club   Proscia Expanded Customer Experience (CX) Practice Enables Users To Realize Value Over 30% Faster   People of Pathology Podcast: Twitter Instagram

Send us a textWhat if we could skip glass slides altogether and go straight from fresh tissue to digital image? Muse Microscopy's SmartPath device aims to do just that, capturing diagnostic-quality images directly from fresh tissue. In this episode brought to you by Muse Microscopy, I sit down with Dr. Rao and Dr. Edwards to discuss the insights, challenges, and future of this groundbreaking technology. We explore its regulatory ramifications, change management in veterinary and human pathology, and financial feasibility. Tune in to learn why SmartPath could be a game-changer for both pathologists and patients.00:00 Introduction to SmartPath Technology00:54 Meet the Experts: Dr. Rao and Dr. Edwards01:08 FDA Approval and Implementation Plans01:35 Change Management in Pathology01:56 Training Pathologists for SmartPath03:48 Translational Tissue Banking and Clinical Applications04:29 Impact on Breast Pathology05:49 Pathologists' Reception and Adoption14:33 Financial Viability and ROI19:44 Conclusion and Future ProspectsLinks and Resources:This episode on YouTubeMuse Microscopy WebsiteSmartPath Device Demo VideoSupport the showBecome a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

Send us a textTransforming Pathology: A Deep Dive into the Muse SystemThis episode is sponsored by Muse Microscopy. In this episode, we explore the primary challenge of implementing digital pathology globally—digitizing the analog. A potential solution is direct-to-digital pathology, exemplified by the MUSE system by Muse Microscopy. This technology eliminates the need for glass slides and manual staining, offering rapid, non-destructive imaging of intact tissue samples. You will learn about the advantages of Muse, including faster diagnostics, improved data fidelity, and broader accessibility, particularly in remote areas. Detailed insights into the Muse workflow, imaging techniques, and potential applications in human and veterinary medicine are provided. Challenges like adoption barriers and regulatory hurdles are also addressed. Join us as we explore how the Muse system is redefining diagnostic workflows and enhancing patient outcomes.00:00 Introduction to Digital Pathology00:18 The Hurdle of Digitizing Analog Pathology00:26 Direct to Digital Pathology: A Game Changer01:46 Introduction to Muse Microscopy02:32 How Direct to Digital Pathology Works03:10 Advantages of Direct to Digital Pathology04:13 Understanding Muse Technology05:26 The Digital Pathology Workflow with Muse14:15 Challenges and Misconceptions15:38 The Future of Pathology16:31 Frequently Asked Questions18:07 Conclusion and Additional Resources18:54 Behind the Scenes and Final ThoughtsLinks and Resources:Original blog post on Digital Pathology Place  Website and LinkedInYouTube playlist with more information about MUSESmartPath Product presentation from CAP 2024Video showing the SmartPath device at the conference boothUSCAP in Boston - Muse Microscopy booth #528Support the showBecome a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

Live from Pathology Visions 2024 in Orlando, FL, the Beyond the Scope team interviews past Presidents of the DPA who were in attendance. Guests include:Eric Glassy (2017)Marilyn Bui (2019)Michael Rivers (2020)Anil Parwani (2021)Ester Abels (2022)Liron Pantanowitz (2023)Junya Fukuoka (Current - 2025)Each President answers questions about the current state of Digital Pathology, what DP/AI looks like in 10 years, how DP/AI can impact healthcare, and how to implement in the community setting. Stay tuned at the end to find out which Presidents are cat or dog lovers and their ice cream preference! It is recommended to watch the video version of this episode on the DPA websiteA forum to engage with the hosts and other listeners has been launched on the DPA website www.digitalpathologyassociation.org. DPA members may login to the DPA Collaborate hub (under the Resources tab) and join the Beyond The Scope community. All listeners are encouraged to use this forum to suggest future topics and guests, submit questions and corrections, and provide general feedback.

Send us a textIn this episode of the Digital Pathology Podcast, I explore the evolving role of Generative vs. Non-Generative AI in Medical Diagnostics. As AI continues to transform the medical field, understanding the differences between these two approaches is essential for pathologists, researchers, and healthcare professionals.We break down the key concepts behind generative AI models (like ChatGPT and image-generation tools) and non-generative AI models (such as traditional machine learning for diagnostic support). I also highlight a groundbreaking seven-part AI review series published in Modern Pathology, which serves as a crucial reference for integrating AI into pathology.

Send us a textIn this episode of the Digital Pathology Podcast, I take a deeper dive into Generative AI in Pathology, following the AI in Pathology series published by USCAP. AI has already begun transforming medical diagnostics, but what does Generative AI mean for digital pathology? From synthetic data generation to multimodal AI models, this episode explores the cutting edge of AI's role in pathology and how it's evolving to enhance efficiency, accuracy, and patient care.

Send us a textIn this episode of the Digital Pathology Podcast, I sit down with Dr. Lija Joseph, a pathologist who is redefining patient care by making pathology more accessible and understandable. Traditionally, pathology has been a “behind-the-scenes” specialty, but Dr. Joseph is changing that by directly engaging with patients, showing them their pathology slides, and empowering them with knowledge about their diagnoses.

In this episode of the Exploring Artificial Intelligence (AI) in Oncology series, Waqas Haque, MD, MPH, Hematology/Oncology Fellow at the University of Chicago, speaks with Osama Khan, MD, Staff Pathologist at Natera, a cell-free DNA (cfDNA) technology company that aims to make personalized genetic testing and diagnostics part of the standard of care. Dr. Khan shares insights into how digital pathology and emerging technologies like AI and measurable residual disease (MRD) testing are revolutionizing oncology, driving precision medicine, and enhancing patient care. Learn more at: https://oncdata.com/digital-pathology-osama-khan

Send us a textWelcome to the 21st edition of DigiPath Digest! In this episode, together with Dr. Aleksandra Zuraw you will review the latest digital pathology abstracts and gain insights into emerging trends in the field. Discover the promising results of the PSMA PET study for prostate cancer imaging, explore the collaborative open-source platform HistioColAI for enhancing histology image annotation, and learn about AI's role in improving breast cancer detection. Dive into topics such as the role of AI in renal histology classification, the innovative TrueCam framework for trustworthy AI in pathology, and the latest advancements in digital tools like QuPath for nephropathology. Stay tuned to elevate your digital pathology game with cutting-edge research and practical applications.00:00 Introduction to DigiPath Digest #2101:22 PSMA PET in Prostate Cancer06:49 HistoColAI: Collaborative Digital Histology12:34 AI in Mammogram Analysis17:21 Blood-Brain Barrier Organoids for Drug Testing22:02 Trustworthy AI in Lung Cancer Diagnosis30:09 QuPath for Nephropathology35:30 AI Predicts Endocrine Response in Breast Cancer40:04 Comprehensive Classification of Renal Histologic Types45:02 Conclusion and Viewer EngagementLinks and Resources:Subscribe to Digital Pathology Podcast on YouTubeFree E-book "Pathology 101"YouTube (unedited) version of this episodeTry Perplexity with my referral linkMy new page built with PerplexityHistoColAI Github PagePublications Discussed Today:

Dr. Steven Hart is a Senior Associate Consultant in AI at Mayo Clinic who has played a key role in shaping genomics and digital pathology with GenomeGPS, Mayo Clinic's primary DNA sequencing workflow. His groundbreaking contributions have led to advancements in understanding inherited cancer risk and improving digital pathology workflows. With over 100 peer-reviewed publications, Dr. Hart's innovative algorithms are driving efficiency in genetic predisposition testing, reducing unnecessary procedures, and enhancing precision healthcare. We had some audio issues for this one which we tried to fix but they're still pretty apparent so apologies for that D:! 00:00:00 - Introduction  00:01:09 - From a factory worker to a leader in AI and medicine 00:05:11 - Proving people wrong as a motivator 00:06:37 - Crazy factory stories 00:07:38 - Why Mayo Clinic? 00:09:52 - Surprising things about Mayo Clinic 00:11:33 - Is Mayo Clinic's data high quality? 00:12:55 - How to prepare healthcare for AI (and why AI won't actually have the biggest impact) 00:20:50 - Democratizing pathology with AI  00:25:38 - Will AI replace pathologists? 00:29:24 - How do you judge how well an embedding works?  00:33:22 - Reducing expectations for diagnostic AI usage in healthcare 00:36:46 - How do you keep up with the rapidly evolving pace of AI? 00:38:31 - OpenAI o1 and prompt hacking  00:41:27 - Are we close to artificial general intelligence?  00:47:03 - How helpful are regulatory agencies like the FDA with translating AI? 00:49:52 - What makes a good question? 00:53:33 - Favorite parts about living in Rochester, MN 00:55:14 - What gives your life meaning?  00:58:36 - Advice for young people in uncertain times Host: Nathan Keller Twitter: @NathanKellerX Linkedin: https://www.linkedin.com/in/nathankeller1/ Producer: Saurin Kantesaria Linkedin: Saurin Kantesaria --- Support this podcast: https://podcasters.spotify.com/pod/show/maml-podcast/support

My guest today is Jon Odle from Pramana. What we discuss with Jon: Overview of Pramana and its scanning technology Importance of QA/QC in digital pathology Challenges in scaling digital pathology, including labor costs The role of AI in digital pathology and its potential as a diagnostic aid The concept of Digital Pathology as a Service (DPAAS) Advantages of DPAAS, including cost-effectiveness and reduced labor The Mayo Clinic project to digitize 12 million slides Challenges faced during the Mayo project, including slide quality issues The importance of interoperability and standardization in digital pathology Future plans for Pramana Links for this episode: Health Podcast Network  LabVine Learning Dress A Med scrubs Digital Pathology Club   Pramana Inside the Digitization of Mayo Clinic's Tissue Registry Archive   People of Pathology Podcast: Twitter Instagram

In this podcast episode, healthcare industry experts from AWS and Philips explore the critical intersection of clinical innovation, cloud technology and healthcare delivery. Ashwini Davison, MD, CMIO, Enterprise Imaging Strategy at Amazon Web Services (AWS), and Martijn Hartjes, Clinical Informatics Business Leader at Philips, delve into the urgent need to ease the burden on clinicians and harness integrated diagnostics to enhance patient care.This episode is sponsored by Amazon Web Services (AWS).