Digital Pathology Podcast

Aleksandra Zuraw, DVM, PhD
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Aleksandra Zuraw from Digital Pathology Place discusses digital pathology from the basic concepts to the newest developments, including image analysis and artificial intelligence. She reviews scientific literature and together with her guests discusses the current industry and research digital pathology trends.

  1. -6 ZILE

    160: AI in Medicine: Neuropathology, Renal Disease, Hematology & Cytology

    Send us a text What 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 Episode Comparison 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 show Become a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

    25 min.

  2. 30 AUG.

    159: What If Your AI Tool Is Lying: Hidden Bias in Pathology Algorithms

    Send us a text What 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 Episode Nature 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 show Become a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

    28 min.

  3. 29 AUG.

    158: Multimodal Magic AI’s Role in Lung & Prostate Cancer Predictions

    Send us a text What 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 Episode World 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 show Become a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

    29 min.

  4. 22 AUG.

    157: How Academic Pathology Programs Can Prepare for AI | UPMC Podcast

    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 translation How AI bias, regulation, and ethics are being addressed — globally What it really takes to operationalize AI in patient care today If you’ve ever asked, “Where do I even start with AI in pathology?” — this is your answer. 🔍 Highlights & Timestamps 00:00 – The importance of earned trust in AI 01:00 – Education gaps in AI for both pathologists & developers 03:00 – Why CPAiCE was built & the three missions it serves 07:00 – The seven-part series: a blueprint for AI literacy 10:00 – Making AI education accessible without losing technical integrity 13:00 – How this series is being used for global teaching (including by me!) 17:00 – Generative AI in creating figures vs. human-authored content 21:00 – Eye-opening global AI regulations that pathologists MUST know 24:00 – Ethics, bias & strategies to mitigate real clinical risks 30:00 – What’s next: CPAiCE’s mission to reshape pathology education & practice 34:00 – A teaser: the first CPAiCE textbook is on the way! 📚 Resources from This Episode 📰 Read the full series (open access!):  Modern Pathology 7-Part AI Series: https://www.modernpathology.org/article/S0893-3952(25)00001-8/fulltext 👨‍⚕️ UPMC’s Computational Pathology & AI Center of Excellence (CPAiCE)  🌍 Creative Commons licensing means YOU can reuse, remix & teach from these resources — just cite the source. Support the show Become a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

    39 min.

  5. 21 AUG.

    156: Digital Pathology and AI in Cancer Grading, T-Cell Imaging & Biomarkers

    Send us a text Can AI Grade Cancer Better Than Us? The Truth About T-Cell Imaging, Biomarkers & Digital Pathology Disruption You 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. 🔬 Highlights with Timestamps [00:04:00] Detecting Metastases with Vision Transformers A team from Leeds Teaching Hospital developed a model for identifying lymph node and omental metastases in ovarian and peritoneal cancers with 99.8% AUROC and 100% balanced accuracy—this isn’t hype; it’s real AI pre-screening that could reduce diagnostic strain on pathologists. [00:08:00] DeepHeme: Bone Marrow Smears Meet AI UCSF and Memorial Sloan Kettering collaborated on DeepHeme, an ensemble deep learning model that classifies bone marrow aspirate cells with expert-level accuracy. With over 30K training images and strong external validation, it outperforms humans in both speed and detail. [00:16:00] Multimodal AI for Head & Neck Cancer This review showcases how integrating radiology, histopathology, and genomics with AI enhances personalized treatment and prognosis. Spoiler alert: Multimodal > unimodal. [00:24:00] Real-Time Kidney Biopsy Evaluation via AI Shoutout to our Digital Pathology Place sponsor, Techcyte, for their AI-powered tool improving accuracy and halving the time it takes to evaluate frozen kidney biopsies. This is the kind of innovation we need in organ transplantation. [00:32:00] GPT-4 as an Oncology Agent? Heidelberg researchers created an autonomous AI agent using GPT-4 plus vision models and OncoKB to handle oncology case decisions with 91% accuracy. This isn’t ChatGPT guessing—it’s a hybrid system citing guidelines and performing complex reasoning. 🧠 Resources From This Episode 📰 Multiple Instance Learning for Metastases Detection in Ovarian Cancer – Cancers journal🧬 DeepHeme: Generalizable Bone Marrow Cell Classifier – Science Translational Medicine📚 AI in Head and Neck Cancer: A Multimodal Review – Cancers journal🧪 AI-Assisted Review of Donor Kidney Pathology – Techcyte & Digital Pathology Place demo🤖 Autonomous AI Agent for Oncology Decisions – Heidelberg Group🎙️ Podcast on GPT-4 agents with Dr. Nina Kolker🧵 Earrings mentioned in the livestream? Find them in the DPP Store I’d love to hear your feedback, your projects, and what digital pathology means to you. You can always reach out through comments, LinkedIn, or email. Support the show Become a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

    35 min.

  6. 20 AUG.

    155: AI Pathology & Genomics_ A New Benchmark for Predicting Gene Mutations

    Send us a text AI Pathology & Genomics: A New Benchmark for Predicting Gene Mutations If 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. 🔬 Episode Highlights & Timestamps [02:00] Abstract 1 – AI + IHC for epithelial cell segmentation in breast cancer [07:30] Abstract 2 – Deep learning quantifies TILs in esophageal cancer [14:30] Abstract 3 – Biopsy size impacts SHGTPF-based liver fibrosis staging [22:30] Abstract 4 – Immunoscore in colorectal cancer: promise & limits 🧬 Key Insights & Takeaways 1. IHC-Guided Segmentation for Breast Cancer Using immunohistochemistry as a ground truth for AI segmentation reveals how effective our models can be—but also where they fall short. The challenge? Accurately subclassifying benign, in situ, and invasive epithelial cells. Spoiler: We’re not quite there yet. 2. Tumor-Infiltrating Lymphocytes in Esophageal SCC A Chinese team trained deep learning algorithms to analyze TILs spatially. Result? High TIL counts in both intra- and peritumoral zones correlated with better survival—highlighting the emerging power of spatial immunology. 3. Liver Fibrosis Staging with SHGTPF Microscopy Second harmonic generation two-photon microscopy gives us label-free imaging of unstained tissue. The takeaway: bigger biopsies (20–26mm) yield better fibrosis quantification. Biopsy position? Surprisingly irrelevant. A game-changer for MASLD diagnostics. 4. Immunoscore for Colorectal Cancer This image analysis-based tool outperforms traditional TNM staging, helping stratify patients for immunotherapy. But adoption is hampered by cost and digital slide access. Integrating AI could take it to the next level—something we should all watch closely. 🎓 Resources from This Episode Breast cancer segmentation using IHC-guided AI (Trondheim, Norway)Esophageal SCC & spatial TILs (Cancer Medicine, China)SHGTPF microscopy in liver fibrosis (UK/US multi-center study)Immunoscore in colorectal cancer (Jerome Galon group origins)💡 Bonus: I show off some histology-inspired earrings and talk about the story behind them—multinucleated giant cells, cartilage, and more. Check them out if you’re into pathology fashion! We’re not just validating AI anymore—we're redefining diagnostics. From high-res, label-free imaging to robust spatial biology insights, the path forward in pathology is clearer and more precise than ever. Whether you’re a practicing pathologist, researcher, or innovator, this episode offers tools and perspectives you can apply today. Support the show Become a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

    23 min.

  7. 19 AUG.

    154: AI in Pathology: Advances in Prostate, Bladder & Endocrine Cancer

    Send us a text If 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 😉 Episode Highlights [06:00] Muse Vet Platform launch + STP talk [11:00] Tools I use: Perplexity, RAG, ChatGPT, and AI citation traps [14:00] AI’s promise—and its pitfalls Paper 1: IHC Scoring in GEC (Caputo et al.) Manual PD-L1 and HER2 scoring is subjective. This study shows AI can standardize and improve accuracy using digital tools for GEC. [20:00] AI reduces visual bias [23:00] Potential to replace expensive assays Paper 2: ASAP in Prostate Biopsies Page Prostate AI matched final diagnoses 85% of the time—more than human reviewers. [24:00] ASAP = gray zone diagnosis [27:00] AI matched final calls more often than humans Paper 3: Recurrence Prediction Post-Cystectomy Chinese study developed a recurrence model using ML on clinical data. AUC: 0.86 (train), 0.77 (test). [30:00] Risk factors: CIS, bladder neck involvement [32:00] SHAP explained model insights Paper 4: Reticulin Framework in Endocrine Pathology Reticulin stains are cheap but powerful. This paper calls for AI to take notice. [36:00] Reticulin separates benign from malignant [40:00] Let’s train AI on these patterns 📚 Resource from this Episode Caputo et al., Pathology Research & PracticePage Prostate study on ASAPML model predicting urethral recurrenceReticulin stains in endocrine tumor gradingAI is already enhancing diagnostic precision—we just need to guide its use responsibly. From special stains to advanced models, this episode covers where we're headed next. Support the show Become a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

    21 min.

  8. 18 AUG.

    153: Can GPT-4o Classify Tumors Better Than Us? AI-Powered Pathology Insights

    Send us a text If we don’t learn to work with LLMs now, we might end up competing with them. 🧠 In this week’s DigiPath Digest, I return to our Journal Club to unpack the latest research on AI in tumor classification, focusing on GPT-4o, LLaMA, and other LLMs. Can these models really outperform traditional tools when analyzing pathology reports? Surprisingly—yes. But don’t panic. This episode is about understanding what LLMs actually bring to the table, how they’re being evaluated, and what we need to consider as digital pathology continues to evolve. It’s also a special week for me personally—I recorded this episode the morning of my U.S. citizenship ceremony, and I used AI to help write my speech! I’ll share more about that next time. ⏱️ Episode Highlights [00:00] – Life update + AI-written speech for my citizenship [04:00] – Journal Club: Austrian study on LLMs in pathology report analysis [05:00] – Why cancer registries need better documentation tools [06:00] – LLMs tested on synthetic pathology reports—game-changing idea [07:00] – GPT-4 and LLaMA outperform score-based models in accuracy [08:00] – Use case: AI-enhanced text mining across whole archives [09:00] – How my PhD could’ve been easier with these tools [10:00] – Second paper: A public synthetic dataset for benchmarking LLMs [11:00] – Tools used: ChatGPT, Perplexity, Copilot to generate report variations [13:00] – Benefits of synthetic data for de-identification [14:00] – Thoughts on bias, annotation workflows, and future-proofing [16:00] – Polish research on hybrid annotation for follicular lymphoma [19:00] – Foundation models, bootstrapping, weak supervision in action [22:00] – Charles River: AI for thyroid hypertrophy scoring in tox path [23:00] – Subjectivity of scoring thresholds and reproducibility [24:00] – Morphology-driven scoring architecture improves accuracy 📚 Resource from this Episode LLM Performance in Malignancy Detection from Pathology Reports 🔗 Read ArticleSynthetic Dataset for Evaluating LLMs in Medical Text Classification 🔗 Read Article🧰 Tools & Topics Mentioned LLMs: GPT-4o, LLaMA, Copilot, PerplexitySynthetic Data for AI model testingAnnotation strategies: weak supervision, bootstrappingPathology AI applications: tumor detection, thyroid activity, lymphomaResearch teams: Austria, Poland, Charles River LabsThe big takeaway? AI tools are improving fast—and it’s up to us to decide how they’re used in our field. This episode breaks down the latest advancements and opens the door to practical, safe integration in pathology workflows. 🎧 Let’s keep pushing the boundaries—together. Support the show Become a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

    20 min.
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Detalii

Aleksandra Zuraw from Digital Pathology Place discusses digital pathology from the basic concepts to the newest developments, including image analysis and artificial intelligence. She reviews scientific literature and together with her guests discusses the current industry and research digital pathology trends.

Informații

  • Creator
    Aleksandra Zuraw, DVM, PhD
  • Perioadă activă
    2019 - 2025
  • Episoade
    160
  • Evaluare
    Adecvat
  • Copyright
    © 2025 Digital Pathology Podcast
  • Site web emisiune
    Digital Pathology Podcast

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