Digital Pathology Podcast

• 162: How Color Impacts Every Diagnosis |Color Calibration in Digital Pathology w/ Tom Kimpe (Barco) and Monika Lamba Saini

  Send us a textWhat if up to 35% of the diagnostic color data on your pathology slides never reaches your eyes—just because of your monitor? In this episode, sponsored by Barco, I sit down with Dr. Monika Lamba Saini (ADC Therapeutics) and Tom Kimpe (Barco) to uncover why color calibration in digital pathology isn’t optional anymore—it’s critical for diagnosis, efficiency, and AI readiness.Highlights:[00:03:42] Monika’s path from CROs to biopharma and why color consistency matters in clinical trials.[00:09:22] What “color science” means in pathology and why color is one-third of diagnosis.[00:12:40] When the same tissue looks different across labs and scanners—and how this causes diagnostic conflicts.[00:16:19] Why HER2 scoring and IHC rely on color intensity—and how poor color fidelity lowers diagnostic confidence.[00:18:34] Research showing up to 35% of H&E slide colors fall outside of the sRGB color space—meaning you never see them on a standard monitor.[00:22:23] Where the biggest sources of color variability occur across the imaging chain come from.[00:26:26] Calibrated displays and pathologist speed—why confidence = faster reads.[00:35:19] How monitors degrade over time and why calibration is essential.[00:41:27] Why choosing a monitor based on price is short-sighted—and the real ROI of medical-grade displays.[00:43:45] ICC profiles explained: the missing piece in end-to-end color consistency.[00:52:48] Training pathologists on color literacy and internal calibration strategies.[01:00:10] How color variability affects AI algorithm accuracy—up to a 30% drop if scanners differ.[01:14:57] The role of professional societies in building color literacy and regulatory guidance.[01:22:30] Final takeaways: if you’re skeptical about calibration, here’s why you should care.Resources from this EpisodeFDA Research by Cheng – H&E slide colors beyond sRGB Reproducible Color Gamut of Hematoxylin and Eosin Stained Images in Standard Color Space. Barco White Paper – The Importance of Color in Modern Pathology.Barco eBook – Digital Pathology: What Are The BenefitsBarco MDPC-8127 Monitor – Medical-grade display optimized for pathology.Digital Pathology 101 (by me, Dr. Aleksandra Zuraw) – Free PDF & Amazon print edition.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

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• 161: 7 Secrets to Smarter AI in Cancer Care | Lessons from NCCN Summit

  Send us a text7 Counterintuitive Secrets from NCCN’s 2025 AI in Cancer Care SummitWhen the National Comprehensive Cancer Network (NCCN) gathers healthcare leaders, people listen. I attended the 2025 Policy Summit on the evolving AI landscape in cancer care—and walked away with insights that were raw, practical, and surprisingly hopeful.Instead of hype or overpromising, cancer care leaders shared honest strategies for implementing AI responsibly and effectively. In this episode, I break down the 7 counterintuitive secrets they’re using to fast-track adoption—while others remain stuck.Whether you’re in digital pathology, oncology, or healthcare AI, these lessons matter for your projects.KEY HIGHLIGHTS0:04 – Reporting from Washington DC: what the NCCN AI Policy Summit revealed about the real state of AI in cancer care.1:10 – Why NCCN guidelines shape cancer care worldwide.1:36 – Even top cancer centers struggle with AI implementation—why delays and budget overruns are common.3:16 – Secret #1: Stop chasing perfect AI tools—build strategic guardrail frameworks instead.6:20 – Secret #2: Plan for biological drift from day one.9:29 – Secret #3: Target underutilized care areas, not your strongest programs.12:07 – Secret #4: Design AI for patients receiving care, not just providers giving it.16:29 – Secret #5: Follow the pioneers—don’t reinvent from scratch.19:09 – Secret #6: Build flexible systems for evolving regulatory pathways.22:09 – Secret #7: Stop using human-level performance as the gold standard.31:23 – Why integration is now as important as innovation in AI for pathology.34:31 – What’s next: NCCN will publish a report based on these discussions.THIS EPISODE'S RESOURCESNCCN – National Comprehensive Cancer NetworkEpisode with Dr. Lija Joseph on patient-pathologist communicationAeffner F. et al. – The Gold Standard Paradox in Digital Image Analysis: Manual vs Automated Scoring as Ground TruthArtera AI FDA de novo authorization news (August 2025)Maryland AI Regulation (effective October 1, 2025)If this episode resonated with you, please share it with colleagues. Speaking the same language around digital pathology and AI implementation will help us all move forward.🎧 Thank you for trailblazing with me. Until next time, keep trailblazing however you can.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

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• 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 showGet 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 showGet 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 showGet the "Digital Pathology 101" FREE E-book and join us!

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