Digital Pathology Podcast

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

  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!

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

  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!

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

  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!

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• 158: AI in Pathology: From Pixels to Patients with Dr. Anil Parwani

  Send us a textHow can pathology labs keep up with rising cancer diagnoses when the workforce is shrinking?  Dr. Anil Parwani believes the answer lies in digital pathology powered by AI—and in this episode, he shares how his team at Ohio State University is making it work today.Amid increasing demands and workforce shortages, pathology is embracing digital transformation. The Ohio State University, for instance, has scanned over 4.2 million slides since 2016, leveraging digital pathology for standardization and objectivity. Current AI applications aid in biomarker quantification, rare event detection, and tumor classification, with future innovations expected in virtual staining, 3D pathology, and large language model integration. While integration challenges remain, these digital tools are poised to augment, rather than replace, human expertise, allowing each institution to navigate its unique "digital pathology chasm" with available market solutions.Episode Highlights with Timestamps:[00:02:15] From glass slides to digital workflows: why the shift was inevitable.[00:05:40] Whole slide imaging: achieving diagnostic quality equal to traditional microscopy.[00:12:22] AI in action: biomarker quantification, rare event detection, and tumor classification.[00:18:50] The next challenge—integrating AI seamlessly into LIS.[00:24:05] Virtual staining and 3D pathology: cutting costs and expanding insights.[00:32:10] Large language models: chatbots as diagnostic assistants and education tools.[00:39:00] Why human expertise remains irreplaceable in complex cases.[00:44:15] Global disparities: how to democratize digital pathology adoption.[00:50:30] The future: autonomous AI-assisted diagnostics and precision medicine.Resources from this Episode:Epredia Digital Pathology: https://www.epredia.com/products/digital-pathology Ohio State University Wexner Medical Center: pathology.osu.edu Support the showBecome a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

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• 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 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.🔍 Highlights & Timestamps00:00 – 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 showBecome a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

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