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Digital Pathology Podcast

Aleksandra Zuraw, DVM, PhD

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.

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  • 27 episodes
  • Avg 36 min
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  • #250
    Thursday · 21 min

    250: AI for Scientific Writing: 4 Rules from a Pathologist

    Send us Fan Mail Can you use AI to write scientific papers without fabricated references, journal trouble or learning to code? I recorded this episode at the airport on my way to ESTP 2026 in Kraków. It's my practice run of the talk I gave there. When deep learning came into pathology, people asked me if I was going to learn to code. I decided no. Large language models kind of proved me right. In this episode I walk you through how I use LLMs for writing papers, responding to co-authors, building slides and even running code, and the four rules that keep it safe: Domain expertise is the prompt Supply the sources Encode corrections The subject matter expert verifies In this episode: What the LLM does vs. what the pathologist does, task by task What Toxicologic Pathology, Veterinary Pathology and Elsevier journals require when you disclose AI use Pathology LLM tools already working on tox study reports, image analysis and whole slide images The reference I didn't check that almost cost me a publication Why an LLM will happily justify switching patients from Tylenol to acetaminophen Automation bias, model bias, and how to start with the tools your institution has approved Going to PathVisions 2026 in San Diego, Oct 16-18? Find me at the Hamamatsu booth #201. I'm also recording 8 short podcast interviews on site, Saturday and Sunday. Share your digital pathology story, grab a slot: https://calendar.google.com/calendar/appointments/schedules/AcZssZ0yjFhaL0kGJ7bZ18oITwyw-qY2yxvcDEyfqpj3LBulrRdiO4fRUkgM0Avlj_oJkIaw-0ZjhaC9 Links Free book, Digital Pathology 101: https://www.aleksandrazuraw.com/book Related episode: How to teach AI to healthcare professionals, with Dr. Candice Chu: Watch the video version on YouTube: https://youtu.be/eY7UywT-zRM?si=qK88eECJqoA-DqKF References Zuraw & Aeffner, Vet Pathol 2022. doi:10.1177/03009858211040484 Turner et al., Toxicol Pathol 2021. doi:10.1177/0192623321990375 Thirunavukarasu et al., Nat Med 2023. doi:10.1038/s41591-023-02448-8 Lu et al., J Transl Med 2026. doi:10.1186/s12967-026-08829-0 Doktorova et al., Arch Toxicol 2026. doi:10.1007/s00204-026-04414-y Trost et al., Nat Med 2026. doi:10.1038/s41591-026-04357-y Chen Y et al., Nat Cancer 2026. doi:10.1038/s43018-026-01220-4 Chu, Front Vet Sci 2024. doi:10.3389/fvets.2024.1395934 Jain et al., Cureus 2025. doi:10.7759/cureus.81618 Kurland et al., Neurosurgery 2025. doi:10.1227/neu.0000000000003354 Painter et al., JAMIA Open 2025. doi:10.1093/jamiaopen/ooaf003 Chen S et al., NPJ Digit Med 2025. doi:10.1038/s41746-025-02008-z Dratsch et al., Radiology 2023. doi:10.1148/radiol.222176 Zack et al., Lancet Digit Health 2024. doi:10.1016/S2589-7500(23)00225-X FDA Warning Letter 722591, Apr 2026 EMA/FDA, Guiding principles of good AI practice in drug development, Jan 2026 Huang & Chu, Front Vet Sci 2026. doi:10.3389/fvets.2026.1801756 Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #249
    August 31 · 30 min

    249: Cytopathology AI: Crowded-Cell Gaps and LLM Guardrails

    Send us Fan Mail What happens when AI models that perform almost perfectly on scattered cervical cells become less reliable than a coin flip on crowded cell groups? In DigiPath Digest #51, I examine what this performance gap tells us about artificial intelligence in cytopathology. The first paper evaluated six convolutional neural network models trained to distinguish benign from high-grade lesions using scattered cervical cytology cells. The models achieved AUCs ranging from 0.950 to 0.996 on the original images. When the same models were applied to hyperchromatic crowded cell groups without retraining or threshold recalibration, their AUCs fell to 0.385-0.683. Different architectures also failed differently. Some overcalled benign clusters, while others missed high-grade clusters. This isn’t simply a technical problem. It illustrates a practical rule for pathology AI: a model should only be trusted for the morphology, specimen type, imaging system, and intended use on which it has been directly validated. I also review the current evidence for large language models in cytopathology. Potential applications include structured reporting, diagnostic support, quality control, education, research, and workflow integration. Some early results appear promising, but each comes with important limitations. One structured-reporting application achieved 99.4% accuracy at a single institution. A diagnostic-support model included the correct answer among its top 10 differentials in 59.1% of general medicine cases. A hybrid quality-control system flagged 84% of errors associated with amended reports, but its false-positive rate wasn’t reported. Most importantly, the review found no language model specifically trained and clinically validated on cytopathology reports. The takeaway is straightforward: we’re still working with narrow AI. Strong performance in one setting doesn’t guarantee performance when the cells, preparation, scanner, institution, or clinical task changes. Low-risk applications may offer the most practical starting point. Text extraction, completeness checks, report consistency review, and quality-control flagging could reduce repetitive work without asking an unvalidated model to make the final diagnosis. Highlights with timestamps 00:00 - Welcome to DigiPath Digest #51 and the new lunch-and-learn time 01:40 - How two image models performed worse than a coin flip on cell clusters 02:29 - Image models, language models, and vision-language models 04:40 - Why AI adoption in cytopathology remains low 05:25 - Scattered single cells versus hyperchromatic crowded cell groups 08:14 - AUCs fall from 0.950-0.996 to 0.385-0.683 10:02 - How ResNet-50 and GoogLeNet failed differently 11:04 - What the attention maps revealed 13:22 - The intended-use lesson for pathology AI 17:02 - Current applications of large language models in cytopathology 18:39 - Structured reporting and the 99.4% accuracy result 19:32 - Diagnostic support, AMIE, and the top-10 limitation 21:09 - Quality-control applications and the missing false-positive rate 23:30 - Why cytopathology still needs domain-specific language models 24:54 - Retrieval-augmented generation, education, and research support 27:07 - Two AI families, one requirement: direct validation 28:35 - Context of use and intended-use validation 29:12 - Human-reviewed training data and destructive book scanning 32:02 - FDA and European approaches to evolving AI models 35:00 - Protecting patient and practitioner well-being 36:21 - Cytopathology-specific benchmarks and shared test sets 38:16 - Why low-risk AI applications should come first 39:50 - Cytopathology’s direct-to-digital advantage 40:39 - Digital Pathology 101 and Pathology Visions Resources from this episode Watch DigiPath Digest #51 Diagnostic performance of AI models trained on scattered single-cell images Leveraging large language models to enhance cytopathology FDA guidance on AI and context of use FDA guidance on predetermined change control plans Google Research: AMIE diagnostic medical AI EU In Vitro Diagnostic Medical Devices Regulation EU Artificial Intelligence Act Digital Pathology 101 Pathology Visions 2026 Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #248
    August 20 · 53 min

    248: Are Foundation Models Really Better for Digital Pathology? Podcast with Panu Kauppila

    Send us Fan Mail What good is a powerful foundation model if it slows the pathologist down, can’t explain its result, or doesn’t fit the clinical workflow? Foundation models are gaining attention across digital pathology. But they’re not finished clinical tools by themselves. In this episode of the Digital Pathology Podcast, I speak with Panu Kauppila, Chief Product Officer at Aiforia, about what foundation models are, how they differ from traditional convolutional neural networks, and what it takes to make them useful for pathologists. Panu describes a foundation model as a large, context-aware building block. To perform a specific pathology task - such as grading, segmentation, or mitotic counting - it must be combined with an adapter, a task-specific head, curated annotations, a usable interface, and integration with the laboratory workflow. We also discuss one of the biggest practical constraints: speed. A pathologist shouldn’t have to click a button and wait for an analysis. Panu explains why AI should run automatically in the background so the results are already available when the case reaches the pathologist’s worklist. The conversation also examines the tradeoff between model size, computational cost, and clinical performance. Larger models may improve robustness and generalizability, but they can also require more processing power. For clinical applications, Aiforia focuses on smaller and medium-sized foundation models that provide the necessary quality without making the workflow slower or unnecessarily expensive. Annotated data remains central. The foundation model supplies the underlying image understanding, while the task-specific head and controlled annotations determine how the model performs on a particular pathology problem. This structure also raises important questions about bias, data provenance, ownership, regulatory documentation, and explainability. Finally, we look at multimodal AI models that combine pathology images with reports, genomic data, molecular information, and clinical outcomes. These tools could support more interactive, predictive, and prognostic applications - but only if they’re introduced through secure, controlled workflows with clear audit trails. Episode Highlights 00:00 — Where does bias enter a foundation model workflow? Panu distinguishes the underlying foundation model from the annotated dataset used to build the task-specific application. 00:27 — Meet Panu Kauppila An introduction to Aiforia’s Chief Product Officer and the episode’s focus on foundation models in digital pathology. 01:05 — From radiology AI to digital pathology Panu describes his background in medical device development, radiology, oncology solutions, and clinical AI implementation. 06:29 — Foundation models versus convolutional neural networks What makes foundation models more context-aware, robust, and generalizable across image datasets. 07:07 — Image-only and multimodal foundation models Why these two categories offer different capabilities and potential clinical uses. 07:50 — A foundation model is a platform, not a finished solution The underlying model may understand image features, but it still needs a task, interface, and clinical workflow. 08:34 — Foundation model, adapter, and task-specific head How these components work together to create an application for grading, mitotic counting, or another pathology task. 09:57 — The cost of larger models Why increased robustness must be balanced against computational demands, inference speed, and affordability. 11:12 — Pathologists won’t wait for AI Why even short delays can interrupt the clinical workflow. 11:46 — Running AI in the background A workflow in which slides are scanned, analyzed automatically, and added to the worklist with results ready for review. 12:16 — Combining foundation models with curated annotations How smaller task-specific datasets and adapter technology can produce practical pathology models. 15:24 — Generalizability across scanners, laboratories, and populations How foundation models may make adaptation to new domains more manageable. 16:36 — Two datasets, two sources of potential bias The regulatory questions created by an underlying foundation model and a separate controlled annotated dataset. 20:46 — Making foundation models accessible Why a platform and user interface are necessary for pathologists and researchers who don’t work directly with code. 21:57 — Testing foundation models in Aiforia Create How researchers can compare a CNN with supported foundation models in the same no-code environment. 25:45 — How foundation models are selected Quality, licensing, model size, annotated data, and the requirements of the intended use. 26:57 — Training cost versus inference cost Why a more expensive training iteration may still reduce total development costs if fewer iterations are needed. 32:23 — Pathologists are visual reviewers The importance of segmentation quality and showing exactly what the model identified. 36:26 — The potential of multimodal AI Combining pathology images with text, genomic information, molecular data, and clinical outcomes. 37:35 — Keeping multimodal AI inside a controlled environment Privacy, security, regulatory oversight, and the risks of moving clinical information into consumer AI tools. 43:24 — Explainability in clinical pathology AI Using semantic segmentation, object detection, instance segmentation, and annotated ground truth to show how results were calculated. 47:43 — Moving toward predictive and prognostic models How established digital workflows could allow pathologists to contribute more information about likely outcomes. 49:45 — Research and clinical collaboration with Aiforia How interested researchers and laboratories can connect through the Aiforia website. Resources Mentioned Aiforia Aiforia Create no-code model-development environment PathChat Listen to the full discussion to understand what foundation models can add to digital pathology—and what still has to happen before they become practical, trusted clinical tools. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #247
    August 17 · 28 min

    247: Screening Efficiency Over Experience: Rethinking Cytology Expertise

    Send us Fan Mail Does more experience automatically make a cytotechnologist more accurate—or does where they look first matter more? In DigiPath Digest #50, I review a digital cytology eye-tracking study that challenges the assumption that diagnostic accuracy improves steadily with years of practice. The researchers tracked the visual behavior of 100 board-certified cytotechnologists with 1 to 40 years of experience. They found no statistically significant linear relationship between years of experience and diagnostic accuracy. Instead, low-power field efficiency—the ability to identify an important target quickly within a wider field—emerged as the key predictor of high accuracy discussed in the study. The study also examined whether this visual skill can be developed. Twenty-eight students completed an intensive three-month cytotechnology training program. After training, they located diagnostic targets more quickly and spent less attention on normal, nondiagnostic cells. In other words, they learned both where to look and what to disregard. What could this mean for digital pathology education? As AI-assisted workflows take on more of the exhaustive searching, cytotechnologists and pathologists may increasingly work as expert verifiers. That requires rapid target assessment, strong knowledge of normal morphology, and awareness of risks such as confirmation bias and cognitive fatigue. The study has an important limitation: it used static images rather than dynamic whole slide imaging. The findings raise useful questions about visual expertise, training, and competency assessment, but they shouldn’t be generalized beyond the study design without further research. Episode Highlights 00:00 – Welcome to DigiPath Digest #50 and introduction to the paper 04:10 – Why the traditional definition of professional expertise is changing 07:02 – Moving from exhaustive searching to verification in AI-assisted workflows 09:04 – How eye tracking was used with 100 board-certified professionals 10:25 – Years of experience versus diagnostic accuracy 13:13 – Experience-based caution and attention to sample information 15:16 – Low-power field efficiency as a predictor of high accuracy 17:05 – Practical low-power field demonstration using a whole slide image 20:15 – Searching versus detecting and the mental map of normal morphology 24:04 – Comparing high- and low-performer visual scan paths 25:27 – Cognitive filtering: knowing what not to examine 27:35 – Can visual efficiency be taught in three months? 29:55 – How AI may shift the human role from searcher to verifier 30:38 – Study limitations: static images versus dynamic whole slide imaging 32:37 – Could gaze efficiency influence future competency assessment? 33:44 – Digital pathology learning resources and closing thoughts Resources Mentioned Abstract and paper: Screening Efficiency Over Experience: Rapid Target Detection in Low-Power Field as a Modifiable Cognitive Biomarker for Diagnostic Accuracy in Digital Cytology Listen to the full DigiPath Digest #50 recording to examine what the study found, what it didn’t prove, and how visual search skills could influence digital cytology training. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #246
    August 13 · 1 hr 7 min

    246: Computational Pathology Is Changing Companion Diagnostics

    Send us Fan Mail Can a treatment decision depend on whether one pathologist sees 45% biomarker positivity and another sees 55%? Visual immunohistochemistry scoring helped establish precision oncology. But as targeted therapies become more sensitive to subtle biological differences, categorical scores such as 0, 1+, 2+, and 3+ may no longer capture the information needed to identify the right patients. In this episode, I speak with three Roche experts: Gordana Juric-Sekhar, MD, anatomic pathologist Saleh Miri, PhD, Director of Digital Pathology AI Algorithms Purvi Gaglani, Regulatory Affairs Lead for Digital Pathology We discuss how computational pathology is changing companion diagnostics by moving biomarker assessment from visual estimates to continuous, cell-level measurements. The conversation examines the limitations of manual IHC scoring, including interobserver variability, intraobserver variability, visual fatigue, borderline cases, tumor heterogeneity, and the inability of the human eye to measure complex spatial relationships. Using TROP2 scoring in advanced non-small cell lung cancer as an example, Saleh explains the normalized membrane ratio. This computational metric measures protein expression at the cell membrane relative to total expression within the cell—something that can’t be reproduced through conventional visual scoring. We also clarify the difference between computer-assisted scoring and a fully computational companion diagnostic. An assisted tool supports a pathologist’s visual interpretation. A computational CDx generates the biomarker measurement through algorithmic, cell-level analysis. That doesn’t remove the pathologist. Pathologists remain responsible for evaluating tissue quality, staining quality, scan quality, tumor selection, image analysis results, and the final clinical context. They can reject a stain, request a rescan, exclude inappropriate regions, question the result, or seek a second opinion. The episode also examines the regulatory implications of computational companion diagnostics. Instead of evaluating a single IHC assay, regulators may need to assess the complete system - from tissue preparation and staining to scanning, image management, algorithmic analysis, display, and the final biomarker report. Finally, we discuss what laboratories will need to implement these workflows, including validated scanning infrastructure, cybersecurity, tighter preanalytical process control, and training that helps pathologists interpret continuous computational measurements. Episode Highlights 00:00 — Pathologists remain central to computational CDx Why computational tools provide more precise measurements without replacing pathology expertise. 01:09 — Why companion diagnostics are changing Visual IHC scoring helped launch precision oncology, but the model is approaching its limits. 04:53 — The current companion diagnostic landscape How IHC, next-generation sequencing, liquid biopsy, and visual biomarker scoring are used today. 07:01 — The mathematical burden placed on the human eye Why manually assessing tens of thousands of tumor cells requires pathologists to estimate rather than calculate. 08:39 — The borderline patient dilemma A digital tool can distinguish measurements such as 74% and 76%, while that difference is difficult to reproduce visually. 09:27 — Why spatial context matters Computational pathology can measure biomarker heterogeneity, clustering, and relationships between tumor and immune cells. 12:17 — Where manual scoring reaches its limits Interobserver variability, intraobserver variability, fatigue, staining interpretation, and heterogeneous tumors. 18:29 — Moving from judgment calls to quantified measurements Why the next stage of precision oncology requires information beyond human visual perception. 19:14 — Computer-assisted scoring versus computational CDx The important distinction between helping a pathologist calculate an existing score and generating a new algorithmic measurement. 23:22 — Computational pathology and decentralized workflows How digital images can support remote review, access to expertise, and second opinions. 27:48 — Why therapies require higher-resolution biomarkers Modern targeted treatments may respond to biological differences that categorical scoring can’t capture. 32:09 — TROP2 in advanced non-small cell lung cancer The episode’s example of a biomarker requiring computational measurement. 33:26 — Understanding the normalized membrane ratio How the algorithm measures membrane expression relative to total protein expression at the individual-cell level. 35:46 — Working with regulators on a new diagnostic model Purvi discusses global health authority engagement and the FDA Breakthrough Device Designation. 38:33 — The computational CDx as a system of systems Why staining, scanning, image management, algorithms, displays, and reporting must be evaluated together. 40:11 — Changes to validated workflow components How using a different scanner, monitor, or other component could fall outside the defined device configuration. 43:30 — Why computational pathology is becoming necessary Continuous measurements can reveal biomarker-treatment relationships that may remain hidden within categorical scores. 49:12 — The pathologist’s role in the workflow Reviewing sample, staining, scan, image, algorithmic analysis, and the final biomarker result. 53:39 — Digital second opinions How image management systems can simplify collaboration without physically transporting glass slides. 56:43 — What laboratories need to prepare Validated infrastructure, cybersecurity, preanalytical control, training, and digital pathology literacy. 58:49 — Learning to interpret computational results The shift from visually estimated categories to continuous, quantitative biomarker measurements. Resources Mentioned Full discussion on YouTube: https://youtu.be/oKW1xC6TTZg Listen to the full discussion to understand how computational pathology could change companion diagnostics—and what pathologists, laboratories, and regulators must prepare for next. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #245
    July 29 · 45 min

    245: Why Going Slow Is Killing Digital Pathology Adoption | Syed T. Hoda, M.D.

    Send us Fan Mail Is your digital pathology rollout moving so slowly that it’s creating a fragmented workflow instead of transforming the department? In this episode of the Digital Pathology Podcast, I speak with Dr. Syed Hoda, Director of Digital Pathology at NYU, about why gradual implementation may no longer be the best approach to digital pathology adoption. Dr. Hoda explains how NYU used an intensive nine-month planning period to prepare for a department-wide transition. The process involved pathology, IT, project managers, vendors, hospital leadership, and approximately 40–50 people participating in regular planning calls. This wasn’t simply a scanner installation. The team mapped workflows, configured Epic Beaker, redesigned laboratory spaces, tested integrations, planned training, and addressed the practical concerns of nearly 100 pathologists. We also discuss why scanner specifications may matter less than integration, vendor support, training, and system performance. For Dr. Hoda, digital pathology had to work as smoothly as glass microscopy. Speed was non-negotiable. Change management played an equally important role. Through open discussions, town halls, and the ADKAR framework, the team addressed concerns ranging from ergonomics to the loss of collaborative microscope sessions. The result? Every pathologist adopted the digital workflow, no one left the department because of the transition, and approximately 60–65 pathologists now work remotely using equipment that matches their office setup. Finally, we examine the next step: artificial intelligence in pathology. Dr. Hoda explains why NYU focused on building a reliable digital foundation before introducing AI. He also raises important questions about validation, transparency, responsibility, regulatory clearance, and the need for greater pathologist involvement in AI development. Episode Highlights 00:00 — Are we repeating the same mistakes with pathology AI? Dr. Hoda compares the current excitement around AI with the early promises made about digital pathology 15 years ago. 01:04 — Meet Dr. Syed Hoda His clinical pathology background and path to becoming NYU’s Director of Digital Pathology. 03:16 — Why going slowly can hold departments back How partial adoption creates fragmented workflows, inconsistent training, and prolonged implementation. 06:25 — Leadership support for rapid adoption Why institutional commitment, resources, and an ambitious timeline made the project possible. 10:13 — Nine months of detailed planning Workflow mapping, laboratory changes, system configuration, vendor selection, testing, and validation. 11:48 — The role of professional project management Why pathologists shouldn’t be expected to coordinate every part of a complex digital transformation. 14:29 — Why the scanner isn’t the most important decision Image quality matters, but integration, service, training, and workflow fit may matter more. 17:42 — People matter more than machines How vendor relationships and departmental engagement supported adoption. 19:19 — Setting clear expectations across the department NYU communicated that every pathologist would move to digital sign-out within a defined period. 20:49 — Change management is a structured process How the ADKAR framework guided communication, education, adoption, and reinforcement. 25:07 — Addressing practical and personal concerns From mouse ergonomics to preserving collaborative case review between pathologists. 27:19 — Why NYU didn’t introduce AI first Dr. Hoda explains why pathologists needed to become comfortable with the digital platform before adding new AI tools. 29:26 — Digital pathology and remote sign-out Approximately 60–65 pathologists now work remotely with equipment matching their office setup. 30:28 — Why speed is non-negotiable Even a small delay or repeated pixelation can quickly undermine confidence in a digital workflow. 33:25 — A cautious approach to pathology AI Concerns about premature adoption, self-validation, limited regulatory clearance, and lack of pathologist involvement. 37:27 — Scientific validation, transparency, and responsibility What happens when the AI result and the pathologist’s interpretation don’t agree? 40:41 — Where AI could meaningfully augment pathology Quantifying microenvironments, feature combinations, ratios, and findings that are difficult to assess visually. Resources Mentioned ADKAR change management framework Digital Pathology Association Executive War College FDA list of AI-powered medical devices A radiology mock-trial paper examining responsibility when clinicians use AI: Examining perceptions of liability about AI in radiology (MedRxiv) Why AI cannot do good science without humans (Nature Editorial) A previous Digital Pathology Podcast discussion about AI-supported colorectal cancer feature analysis (How to use deep learning image analysis for colon cancer with Rish Pai) Listen to the full conversation for a practical look at digital pathology planning, change management, remote sign-out, scanner integration, and responsible AI adoption. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #244
    July 22 · 1 hr 37 min

    244: Why AI Still Hasn't Revolutionized Drug Discovery (Yet) | Thibault Geoui, PhD

    Send us Fan Mail If AI is already being used across the drug development pipeline, why hasn’t its impact matched the investment? AI can help researchers review scientific literature, predict protein structures, prioritize molecules, assess toxicity, support clinical trials, and monitor adverse events. But access to better tools doesn’t automatically create better drugs. In this episode, I speak with Thibault Geoui, Science CDO and host of the Tech & Drugs Podcast, about where AI is making a practical difference in drug discovery and development—and where the results remain limited. We map AI across the full drug development funnel, from basic research and target identification to preclinical testing, clinical trials, regulatory documentation, commercialization, and pharmacovigilance. We also discuss why digital-native tech-bio companies may be better positioned to benefit from AI than traditional pharmaceutical organizations. The difference isn’t simply the model. It’s how data, people, laboratory experiments, and AI tools are connected inside the workflow. For digital pathology professionals, the conversation becomes especially relevant when we examine AI-powered biomarker development, the role of pathology in pharmaceutical research, and the Roche–PathAI case discussed in the episode. And, of course, we talk about the problem every AI user eventually faces: an answer can look polished, specific, and completely convincing—and still be wrong. Episode Highlights 00:00 — When convincing AI output creates more work Why AI can accelerate information generation while increasing the time required for review and verification. 02:15 — From structural biology to science and technology leadership Thibault shares his background in X-ray crystallography, structural biology, scientific data, and digital product development. 15:36 — Understanding the drug discovery and development funnel How thousands of potential compounds are narrowed down through discovery, preclinical research, clinical trials, and approval. 20:00 — AI for scientific literature review How alerts, filtering, summarization, and information extraction can help researchers manage a rapidly growing scientific literature base. 22:32 — AlphaFold and protein structure prediction What faster access to predicted protein structures changes for researchers—and why structural prediction alone doesn’t solve drug discovery. 24:13 — Searching an enormous chemical space How AI can help design and prioritize potential molecules for synthesis and experimental testing. 25:50 — Predicting efficacy and toxicity Where AI supports preclinical research, why the models remain imperfect, and why experimental validation still matters. 29:38 — Has AI changed drug development outcomes yet? A practical discussion about drug approval rates, AI investment, uneven returns, and the difference between deploying a tool and integrating it into a process. 34:33 — Why traditional pharma struggles to scale AI Siloed data, legacy systems, organizational complexity, and the need to build reusable data workflows. 37:57 — The “lab in the loop” model How tech-bio companies connect AI predictions with wet-lab experiments and feed the new data back into their models. 44:37 — Can tech-bio companies shorten development timelines? How digital-native organizations are changing parts of the discovery and preclinical process. 58:00 — AI, pharma, and digital pathology What the Roche–PathAI case discussed in the episode may indicate about the role of pathology data, biomarker discovery, and pharmaceutical workflows. 01:06:17 — AI errors in regulated environments Why responsibility remains with the person or company submitting AI-assisted work, regardless of which tool produced it. 01:17:37 — The growing cost of AI tools Subscriptions, token limits, model selection, AI orchestrators, and the need to use expensive tools more intentionally. 01:27:50 — What successful AI adoption requires Starting with focused pilots, training scientists and technologists together, and treating implementation as organizational change. 01:30:26 — The AI quirks that still frustrate users Hallucinated information, ignored writing instructions, stylistic habits, and poor awareness of time and context. The episode’s timestamped themes and examples are documented in the supplied summary. The broader discussion covers AI from literature mining and molecular design through clinical development and post-market monitoring. Resources Mentioned Thibault Geoui’s LinkedIn profile Tech & Drugs Podcast MIT NANDA study on generative AI implementation and return on investment Insilico Medicine as an example of a digital-native tech-bio company AI is already changing how scientific work gets done. The bigger question is whether organizations can redesign their workflows, train their teams, and maintain the human oversight needed to use it well. Listen to the full episode for a practical look at AI in drug discovery, drug development, and digital pathology. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #243
    July 2 · 44 min

    243: How to Teach AI to Healthcare Professionals | Podcast with Candice Chu

    Send us Fan Mail What does AI literacy actually look like for pathologists, researchers, and future clinicians? And how do you teach it in a way that is practical, not abstract? In this episode, I talk with Candice Chu, DVM, PhD about something I think a lot of people in digital pathology and computational pathology are feeling right now: AI is moving fast, but education is still catching up. Candice is a clinical pathologist, veterinarian, and educator building AI-focused teaching and research at Texas A&M. We worked together before on digital pathology and image analysis projects, so this conversation felt especially grounded. We talk about her AI literacy curriculum framework for veterinary education, why she decided to build it, and what it takes to teach AI in a way that is useful, ethical, and realistic. This episode is about understanding what AI tools are good for, where they can waste your time, and why hands-on experience matters. Candice explains why she sees AI as a set of tools, not a belief system. Try them. Learn them. Keep what improves your workflow. Drop what does not. We also talk about the difference between putting educational content online and building formal institutional teaching. That matters because social media can move quickly, but curriculum changes, research, and professional organizations shape longer-term adoption. Candice shares how her course started as a low-stakes elective, then grew into a more structured framework that combines education with publishable research. A big part of this conversation is the curriculum itself. We go through what students actually learn: AI fundamentals without heavy math, machine learning and image analysis, large language models, prompt engineering, chatbot building, ethics, literature research, and final projects where students evaluate real tools and workflows. I liked that the course does not stop at theory. It asks students to use tools, question them, and explain where they help and where they do not. We also get into something that matters far beyond veterinary medicine: professional responsibility. If AI is involved in a workflow, the clinician is still responsible. That includes fabricated citations, bad outputs, weak prompts, and the temptation to trust tools too quickly. Candice makes a strong case that AI education needs ethics, legal context, and interdisciplinary teaching built in from the start. If you are trying to think more clearly about AI in pathology, education, workflow design, or professional training, this episode gives you a concrete example of what responsible AI literacy can look like. Episode Highlights 00:00 – Why AI tools are just tools, and why trying them matters even if you later decide not to keep using them 00:33 – Who Candice Chu is and why her work on AI literacy in veterinary medicine is worth paying attention to 02:33 – Why going back to Texas A&M changed the scale of Candice’s AI research and teaching 07:53 – How the AI course was designed as a low-stakes elective first, and why that helped student engagement 11:16 – Where veterinary AI education stands now, and what professional organizations like ACVP are doing 13:08 – Why AI adoption in veterinary medicine is still slow, and what skepticism usually sounds like in practice 15:19 – Real examples of how Candice uses LLMs and computer vision in pathology, medical records, and research 19:58 – What is actually inside the 15-week AI literacy curriculum, from fundamentals to final projects 24:16 – Why ethics and legal responsibility are not optional in AI education 31:35 – Why no-code tools and vibe coding are entering the curriculum already 38:50 – The AI tools Candice is testing in her own workflow, including Claude, Codex, and Perplexity Resources mentioned Candice Chu’s AI literacy curriculum framework paper in Frontiers in Veterinary Science Candice’s earlier work on ChatGPT in veterinary medicine Texas A&M and the institutional setting where Candice is building AI research and teaching Mr. Don Riddick and the AVMA AI working group, mentioned in the ethics and legal context Claude, Codex, and Perplexity as AI tools Candice is actively testing Digital Pathology 101, mentioned in the conversation as a teaching resource Candice’s online educational work on Instagram. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #242
    June 24 · 44 min

    242: Foundation Models in Pathology: Strong on Paper, Ready for Labs?

    Send us Fan Mail Are pathology foundation models actually ready for labs, or are they still stronger on paper than in practice? In this episode of DigiPath Digest #49, I unpack a timely review on pathology foundation models and ask the question that matters most to me: not just what these models can do, but what has to be true before they are genuinely useful in real pathology workflows. I walk through how pathology AI moved from narrow, task-specific models into the era of transformer-based foundation models. That shift matters because pathology is no longer only about looking at H&E in isolation. Today, pathologists are expected to integrate morphology, immunohistochemistry, molecular assays, genomics, and clinical context. That growing complexity is one reason foundation models are getting so much attention. In this discussion, I explain how transformers entered pathology, why image patches are treated like tokens, and how shared embeddings can support classification, regression, segmentation, and multimodal retrieval. I also go through the major pathology foundation models mentioned in the paper, including Virchow/Virchow2, Mayo Clinic Atlas, UNI, CONCH, H-Optimus, GigaPath, and TITAN, and why scale alone is not the full story. A big part of this episode is about the gap between benchmark performance and clinical readiness. I talk about the persistent limitations in training data diversity, the overuse of TCGA, and why public benchmarks can still miss what real pathology practice looks like. I also cover where foundation models still struggle, especially in cytopathology, hematopathology, and underrepresented disease areas, along with the real-world problems of artifacts, domain shift, concept drift, infrastructure burden, regulatory complexity, and workflow disruption. For me, one of the most important themes is this: AI in pathology should augment, not replace, pathologists. The future is not about handing diagnosis to a model. It is about building tools that support pathologists better, fit real workflows, and can be validated in ways that deserve trust. I also spend time on what comes next: explainable AI, counterfactual explanations, conversational interfaces, retrieval-augmented systems, multimodal fusion, and the need for deployment-centric validation rather than paper-only excitement. If you are trying to understand where pathology foundation models really stand today, this episode will help you separate the promise from the practical barriers. Episode Highlights 00:01 – Why I chose this paper, what is changing at Digital Pathology Place, and why foundation models are worth paying attention to now. 02:15 – The core questions: what pathology foundation models are, where they are, and how difficult they are to apply in pathology. 04:50 – Why pathology is becoming more cognitively demanding, and how multimodal complexity is driving interest in scalable AI. 07:02 – From narrow AI to transformers: how pathology moved beyond single-task CNN models. 10:16 – How transformers work in pathology: image patches as tokens, self-attention, embeddings, and downstream tasks. 14:16 – Why multimodality matters, and what kinds of data foundation models may eventually integrate. 15:27 – Timeline of key model developments, from “Attention Is All You Need” to gigapixel-scale pathology foundation models. 17:13 – The leading models and what scale really looks like: Virchow, Mayo Clinic Atlas, UNI, CONCH, H-Optimus, and GigaPath. 19:51 – Why dataset diversity matters more than sheer volume, and why TCGA is not enough. 23:17 – Where foundation models still struggle: cytopathology, hematopathology, rare disease, artifacts, scanner shifts, and pen marks. 28:06 – Explainability, counterfactual explanations, and why trust in pathology AI needs more than attention maps. 30:17 – The real deployment hurdles: regulation, infrastructure, workflow fit, and economics. 36:32 – Why AI should augment pathologists, not replace them, and which tedious tasks pathologists would gladly hand over. 38:36 – Retrieval-augmented and conversational AI in pathology: where interactive systems may actually help. 40:51 – Vision-language models and multimodal fusion with histology, radiology, genomics, and clinical notes. 42:16 – The path forward: deployment-centric design, prospective multi-site validation, and human-AI collaboration. 44:08 – Closing thoughts on AI literacy, community learning, and what needs to happen next. Resources Mentioned Main paper discussed: Pathology Foundation Models: Evolution, Current Landscape, Challenges and Opportunities from a Technical and Clinical Perspective https://doi.org/10.3390/bioengineering13050577 Review article / journal landing page: https://doi.org/10.3390/bioengineering13050577 Benchmarks mentioned: PathoBench — discussed in the review paper; use the review link here for context until you want to swap in a canonical project page: https://doi.org/10.3390/bioengineering13050577 PathBench — public benchmark paper: https://arxiv.org/abs/2505.20202 MEDFAIR — benchmark paper: https://arxiv.org/abs/2210.01725 MEDFAIR code repository: https://github.com/ys-zong/MEDFAIR Models mentioned: Model overview in the review (Virchow/Virchow2, UNI, CONCH, H-Optimus, GigaPath, TITAN, Mayo Clinic Atlas): https://doi.org/10.3390/bioengineering13050577 Virchow: https://arxiv.org/abs/2309.07778 UNI: https://arxiv.org/abs/2308.15474 CONCH: https://arxiv.org/abs/2307.12914 Mayo Clinic Atlas: https://arxiv.org/abs/2501.05409 TITAN: https://arxiv.org/abs/2411.19666 Dataset mentioned: The Cancer Genome Atlas (TCGA) https://portal.gdc.cancer.gov/ Book mentioned: Digital Pathology 101: All You Need to Know to Start and Continue Your Digital Pathology Journey https://digitalpathologyplace.com/ Platform: Digital Pathology Place https://digitalpathologyplace.com/ Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #241
    June 17 · 43 min

    241: AI-Powered Companion Diagnostics: The Future of Precision Medicine | Podcast with Doug Bowman, VP Precision Medicine at Indica Labs, Inc.

    Send us Fan Mail How far can pathologists take visual biomarker scoring before human vision becomes the bottleneck? In this episode, I talk with Doug Bowman. PhD, VP Precision Medicine at Indica Labs, about what happens when companion diagnostics move from traditional visual scoring into the era of AI-powered image analysis. Doug comes from a biomedical and electrical engineering background, with experience in microscopy, digital image analysis, pharma workflows, and now precision medicine at Indica Labs. That combination makes him a great person to talk to about how image analysis actually fits into real companion diagnostic development. We start with a very practical question: what is a companion diagnostic, and why is it becoming so important in precision medicine? Doug explains that companion diagnostics are developed alongside therapeutics to help identify which patients are most likely to benefit from a specific treatment, especially in more complex therapies like antibody-drug conjugates (ADCs). We use HER2 as an example, and from there we get into the real challenge: once a biomarker cutoff matters clinically, visual estimation around that cutoff becomes much harder than many people want to admit. That is where this conversation gets especially useful for pathologists and digital pathology trailblazers. We talk about the limits of human vision, why low or ultra-low biomarker expression is difficult to score consistently, and how AI helps at multiple levels of the workflow: slide QC, tissue classification, cell segmentation, membrane and cytoplasmic measurement, and spatial analysis. Doug makes the case that AI is not only a convenience here. In some cases, it is the only realistic way to capture the kind of quantitative information modern therapies need. We also get into one of the more interesting examples from the episode: the Trop2 story, where a ratio of cytoplasmic to membrane expression appears to predict therapeutic efficacy better than looking at one compartment alone. That kind of compartment-level quantitation is exactly where computational pathology becomes more than a digital version of what the eye already does. It starts uncovering measurements and signatures the eye cannot reliably extract on its own. Another important part of the discussion is workflow and regulation. Doug walks through how AI-powered companion diagnostics are developed from preclinical work, to human feasibility studies, to RUO or clinical trial assays, and eventually toward analytical and clinical validation with regulatory engagement happening early. We also talk about the Indica Labs and Leica Biosystems partnership, and why end-to-end capability matters when you are trying to build something clinically deployable rather than just analytically interesting. What I liked about this conversation is that it stayed grounded. We did not talk about AI as magic. We talked about image analysis as a method, companion diagnostics as a workflow, and precision medicine as something that only works when the measurement is good enough to support real decisions. Episode Highlights 00:00 – Why AI matters in slide QC, tissue classification, and cell-level analysis before you even get to the biomarker score. 00:54 – Doug Bowman’s background in biomedical engineering, microscopy, and digital image analysis. 05:16 – What a companion diagnostic actually is, and why it is critical for targeted therapies and ADCs. 07:34 – Why visual biomarker scoring becomes unreliable around critical cutoffs, especially in low-expression cases. 10:09 – How AI expands the workflow: slide QC, tissue classification, and precise cell segmentation. 13:07 – Why pathologists remain central in AI workflows through validation, markup review, and model refinement. 16:31 – The Trop2 example: when cytoplasmic-to-membrane ratio tells you more than one compartment alone. 20:23 – The Indica Labs + Leica Biosystems partnership and why end-to-end workflow matters in companion diagnostics. 22:53 – What the development journey looks like from early algorithm work to RUO, validation, and regulatory interaction. 26:51 – Multiplexing, spatial analysis, and why more clinical value often comes with more deployment complexity. 33:29 – Why image analysis literacy matters, and how shared language between pathologists and scientists becomes essential. 40:13 – Where to learn more about Indica Labs and who to contact for collaboration. Resources mentioned Indica Labs Indica Labs contact – info@indicalab.com HALO software / HALO AI diagnostic image analysis – discussed in the context of companion diagnostic deployment and pharma services. Leica Biosystems GT450DX – referenced as an FDA-cleared slide scanner in the Indica-Leica partnership. Digital Pathology Association – mentioned as part of the broader educational ecosystem for digital pathology and image analysis. Digital Pathology Place / Digital Pathology Podcast – the platform hosting this conversation and related education around digital pathology and AI. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #240
    June 3 · 35 min

    240: Can AI Copilots Keep Up with Pathologists?

    Send us Fan Mail Can AI copilots really keep up with pathologists when the cases are new, the workflow is messy, and the benchmark is actually protected from leakage? In this episode of DigiPath Digest #48, I focus on one paper: DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset. I chose this paper because I think the field needs more of this kind of work. Less hype. More evaluation. Less “look what AI can do.” More “how do we test it in a way that actually means something?” In this session, I look at what makes DALPHIN important for pathologists, lab leaders, and digital pathology trailblazers trying to make sense of pathology AI right now. The paper benchmarks three models against human pathologists: two general-purpose models, Gemini 2.5 Pro and GPT-5, and one pathology-specific model, PathChat+. The dataset includes 1,236 images from 300 cases, covering 130 diagnoses, 14 pathology subspecialties, and cases from six countries. Human performance is benchmarked with 31 pathologists from 10 countries. What I like about this paper is that it does not stop at top-line performance. It deals with the benchmarking problem itself. The authors built a sequestered, indirectly accessible ground truth so the evaluation data could not simply be scraped into model training. That matters because without that protection, benchmarking can become an illusion of genius rather than a real test of generalization. The results are interesting and more nuanced than a simple win-or-lose story. PathChat+ reached expert-level performance in four of six tasks, Gemini in two of six, and GPT in one of six. That tells us something important already: pathology-specific training matters. But it also does not mean pathology is solved. In organ recognition, expert pathologists still outperformed all the models. In rare cancers, none of the models reached expert-level performance. And in ambiguous cases, the models still struggled with something human pathologists do all the time: expressing uncertainty. I also spend time on one of the most practical parts of the paper: model behavior. Gemini tended to overcall. GPT tended to undercall. PathChat was more balanced. That matters in practice. A pathologist using a copilot needs to know the tool’s calibration bias before they can safely interpret what it is telling them. I also talk about anchoring bias in conversational interfaces, where early hallucinations can propagate through later answers if memory is not reset between questions. That is not just a technical curiosity. That is a workflow and safety issue. Why should you listen? Because this episode is really about a bigger question: What kind of evidence should pathologists demand before AI copilots enter real workflows? If you want to understand validation, data leakage, rare-case performance, uncertainty, and why these tools should still be treated as co-pilots rather than autopilots, this is a useful paper to know. Episode Highlights 01:20 – Why I chose the DALPHIN preprint and why benchmarking matters right now. 05:38 – What is in the DALPHIN dataset: 300 cases, 130 diagnoses, 14 subspecialties, 6 countries. 07:57 – Top-line performance: PathChat+ reaches expert-level performance in 4 of 6 tasks. 09:41 – The benchmarking trap of data leakage and why DALPHIN’s sequestered ground truth matters. 12:19 – Why real pathology diagnosis is not text-only and why macro + micro context matters. 15:26 – Tissue recognition, neoplasm detection, ambiguity, and conversational memory: how the testing was structured. 21:29 – The diagnostic personalities of the models: overcalling, undercalling, and balanced behavior. 24:36 – Rare cancers: where AI copilots still fall short of expert human performance. 28:00 – Why binary outputs are not enough when pathology often lives in uncertainty. 31:37 – Anchoring bias and conversational memory: how early hallucinations can keep propagating. 37:11 – Why these tools should be treated as co-pilots, not autopilots. 40:29 – Resources for beginners: Digital Pathology 101 and continued AI literacy. Resources mentioned DALPHIN preprint: arXiv:2605.03544v1 DALPHIN evaluation platform: dalphin.grand-challenge.org PathChat+ pathology-specific AI model discussed in the benchmark. Digital Pathology 101 free eBook by Dr. Aleksandra Zuraw. Educational streams on tissue recognition and computer vision literacy mentioned in the session. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #239
    May 28 · 49 min

    239: The Four Steps Pathology AI Demos Quietly Skip and Why They Matter

    Send us Fan Mail What happens when a pathology AI model misses the tissue before it even begins—and can better segmentation, education, and virtual staining close the gaps? In DigiPath Digest #47, I review four recent studies that expose both the promise and the weak points of AI-assisted digital pathology. We start with a step that sounds simple: detecting tissue on a whole slide image. Yet when that first step fails, the downstream algorithm may miss cancer entirely. From there, I look at a practical resource designed to improve communication between pathologists and computational scientists, a self-refining Segment Anything Model that reduces the burden of pixel-perfect nuclear annotations, and a virtual HER2 immunohistochemistry method generated from H&E images. The common lesson? Digital pathology AI is only as reliable as the workflow underneath it. Tissue processing, scanning, segmentation, annotation, model design, and clinical interpretation all matter. Every step needs validation. Discussion highlights 00:00 – Welcome to DigiPath Digest #47 and an overview of the four papers 02:31 – Why tissue detection is the foundation of pathology AI workflows 05:01 – A large prostate pathology study using 33,823 whole slide images for tissue detection and 70,000 images for downstream Gleason grading 06:28 – Classical thresholding missed tissue completely on 118 slides, compared with 24 slides using U-Net++ tissue detection 07:51 – How artifacts and uneven slide color can disrupt classical detection methods 09:59 – AI-based tissue detection reduced total failures by 79% but didn’t eliminate them 10:39 – Why validating only the final algorithm output can hide upstream sources of error 12:05 – Tissue detection choice produced clinically significant differences in final Gleason grade in 3.5% of malignant slides 13:33 – The communication gap between pathology and computer science—and why it still slows progress 15:29 – “Decoding Digital Histopathology” as an accessible guide for computational researchers entering pathology 18:03 – Digital Pathology 101, my free companion resource for anyone starting or continuing a digital pathology journey 20:18 – From collected tissue to glass slide, whole slide image, interpretation, and computational analysis 25:08 – The role of TCGA and the growth of digital pathology datasets across clinical, veterinary, preclinical, and research settings 27:14 – Why pixel-level nuclear annotation remains a bottleneck for deep learning 30:39 – Replacing detailed nuclear outlines with simple point prompts 31:23 – How the self-refining SAM framework uses sparse labels, contrastive learning, and a correction loop 33:25 – Do we really need to keep annotating the same structures for every new model? 37:49 – Generating virtual HER2 IHC from H&E with a score-aware, non-contrastive multitask model 39:53 – Study results: 83.01% accuracy for virtual IHC alone and 97.85% accuracy when H&E and virtual IHC were combined 40:56 – Where virtual IHC may fit as a complementary or triage tool—and why confirmatory testing still matters 44:06 – My perspective on trust, interpretation, and the difference between predicting IHC and predicting molecular alterations 46:17 – Final thoughts and an invitation to continue the discussion Resources mentioned “Impact of Tissue Detection on Diagnostic Artificial Intelligence Algorithms in Prostate Digital Pathology” – Scientific Reports “Decoding Digital Histopathology: The Building Blocks for Computational Researchers” – PLOS Digital Health “Self-Refining Segment Anything Model for Nuclear Segmentation: A Contrastive Learning Approach to Label-Efficient Pathological Imaging” – Diagnostics “HER2 Score-Aware Virtual Immunohistochemistry via Non-Contrastive Multitask Translation” – Diagnostics Digital Pathology 101: All You Need to Know to Start and Continue Your Digital Pathology Journey European Society of Digital and Integrative Pathology The Cancer Genome Atlas (TCGA) If you work in pathology, computational science, image analysis, or AI development, this episode is a useful reminder to look beyond the headline performance metric. The errors that matter may begin much earlier in the workflow. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #238
    May 19 · 20 min

    238: How Do We Know AI Is Ready for Pathology

    Send us Fan Mail Do you really need a scanner, whole slide images, and AI infrastructure before you can start in digital pathology? In this episode, I argue that you do not. I’m Dr. Aleksandra Zuraw, veterinary pathologist and digital pathology educator, and this talk is about a belief I hear all the time: I don’t have the tools yet, so there is no point learning digital pathology. I used to think that too. When I was training in Berlin, there was one Leica 6-slide scanner, and it felt like digital pathology was only for a small group of chosen people. That experience made the field feel distant, exclusive, and not really available to beginners. What changed for me was not a new scanner. It was a small project. I needed a more consistent way to quantify a senescence marker in archived skin samples, so I used a microscope camera, captured images, opened them in Microsoft Paint, and manually marked cells with colored dots. It was scrappy. Very low tech. But it was also digital, consistent, and verifiable. That project became my first real step into digital pathology and helped me get my first job in the field, where I worked between pathologists and image analysis scientists on biomarker quantification and patient stratification problems. That is the core point of this episode: knowledge unlocks technology. Scanners matter. AI tools matter. But the deeper bottleneck is whether enough people understand how to use these tools, ask good questions, and connect pathology expertise with digital workflows. That is why this episode is really about readiness. Not readiness of the hardware. Readiness of the people. I also talk about Dr. Taladzer from Pakistan, whose story makes this point even more clearly. At the time, Pakistan had around 220 million people, about 500 pathologists, and zero scanners. She still started learning digital pathology during COVID using a microscope and camera, joined the Digital Pathology Association, taught herself from papers and online resources, and kept going even after multiple AI vendors rejected her because she did not have whole slide images. Eventually, she found a DIY image analysis platform, learned to annotate and train models on static images, completed projects quickly, and went on to publish more than 10 digital pathology papers without ever using WSI. Why should you listen? Because this episode is for pathologists and lab leaders who are interested in digital pathology but still feel stuck at the beginning. It is for people waiting for permission, perfect infrastructure, or a formal roadmap. And it is for trailblazers who came back from a meeting or conference energized, but need a practical way to turn that energy into action before it fades. I also address an important AI question near the end: How do we know an AI model is good enough for pathology? I talk about why models are only as good as the pathologist annotations used to train them, why concordance between pathologists matters, how orthogonal labels like IHC can improve model quality, and why pathologists still need to stay in the loop as these systems develop and get deployed. If you are trying to figure out where to start, this episode gives you a practical answer: start where you are. Start with what you have. Start learning now. Episode Highlights 00:00 – Why the real barrier to digital pathology is usually not the hardware 00:33 – What it feels like to be at the beginning of the digital pathology journey 02:50 – My first practical digital pathology project using a microscope camera and Microsoft Paint 05:37 – How that low-tech project led to my first digital pathology job 08:52 – Why knowledge, not infrastructure, is the real unlock 09:57 – Dr. Taladzer’s story: starting digital pathology in Pakistan with zero scanners 12:03 – What happened after repeated vendor rejection and why persistence mattered 14:39 – The “forgetting loop” vs the “commitment loop” after conferences 16:48 – Practical next steps: book, PubMed alerts, journal clubs, webinars, vendor resources 18:52 – Why I believe digital pathology is the gateway to faster diagnosis 20:00 – How to think about whether an AI model is really ready for pathology Resources Mentioned Digital Pathology 101 – free book recommended as a starting point for learning digital pathology. Digital Pathology Association – mentioned as a learning resource and professional community. PubMed alerts for AI and digital pathology. Journal clubs – mentioned as one way to keep learning consistently. Webinars and vendor resources – suggested as practical ways to keep building knowledge. A4A – the DIY image analysis platform that supported Dr. Taladzer’s early work with static image annotation and model training. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #237
    May 18 · 34 min

    237: Why Pathology Vendor's Don't Speak the Same Language?

    Send us Fan Mail Why are pathology vendors still speaking different image languages when radiology solved that problem decades ago? In this episode of DigiPath Digest #46, I talk through four papers that all point to a bigger issue in digital pathology: we are not only dealing with better algorithms. We are dealing with interoperability, workflow design, explainability, and whether the field is actually ready to use these tools well. I start with DICOM in digital pathology, because I think this is still one of the most important infrastructure questions in the field. Digital pathology has clear value for consultation, image analysis, archival, and workflow, but vendor-specific whole slide image formats still create silos. In the episode, I explain why DICOM matters, why adoption is still low, how the multi-resolution pyramid works, and why this is really about enterprise imaging and future-proofing, not just file conversion. Then I move into kidney transplant rejection, where the paper makes a strong case for multimodal precision diagnostics. Creatinine is late. Antibody testing can miss important biology. Biopsies can miss the area that matters. So the opportunity is not to replace pathology, but to combine biomarkers, biopsy, and machine learning in a way that is more useful than any one signal alone. I also talk about explainability here, because if a model gives a risk score, we need to know what contributed to it. The third paper focuses on perineural invasion in solid tumors, and I liked this one a lot because it shows how AI can help standardize something that is clinically important but still inconsistently detected and reported. Perineural invasion is not just a passive pathway of spread. The biology is more active than that, and the quantification can go far beyond a simple yes-or-no answer. This is a good example of where digital pathology can do something humans cannot realistically do by eye at scale. The last paper is on gastric cancer immunohistochemistry biomarkers and advanced quantification, including HER2, PD-L1, mismatch repair, and CLDN18.2. This section is really about complexity. We are now asking pathologists to visually score biology that is getting harder and harder to summarize consistently, especially when markers, spatial context, and multiplexing all start to matter at once. I make the case that computational pathology is becoming necessary here, not because pathologists are failing, but because the biology is outgrowing purely visual workflows. What ties these four papers together is simple: digital pathology is not only about remote reading anymore. It is about interoperability, quantification, explainable AI, and making pathology more precise in places where the old workflow is reaching its limit. If you are a pathologist, lab leader, or digital pathology trailblazer trying to figure out what actually matters right now, this episode will help you connect the dots. Episode Highlights 07:41 – Why DICOM still matters if we want digital pathology systems to work together. 14:39 – Current adoption of SVS, MRXS, and DICOM, and why DICOM is still lagging. 16:44 – How the DICOM whole slide image pyramid works and why it matters for workflow. 24:29 – Why kidney transplant rejection is still difficult to diagnose with any single marker. 29:18 – Why perineural invasion is clinically important and still inconsistently reported. 34:44 – How AI can quantify tumor-nerve relationships more consistently than visual review alone. 46:39 – Why gastric cancer biomarker scoring is getting too complex for purely visual workflows. 54:55 – Multiplexing, spatial biology, and why explainable AI matters in biomarker interpretation. 01:04:01 – What is really blocking digital pathology adoption: cost, workflow, regulation, or mindset? Resources mentioned DICOM / digital pathology interoperability paper https://pubmed.ncbi.nlm.nih.gov/42093730/ Kidney transplant rejection, biomarkers, and artificial intelligence https://pubmed.ncbi.nlm.nih.gov/42073482/ Perineural invasion in solid tumors with AI and machine learning applications https://pubmed.ncbi.nlm.nih.gov/42100436/ Gastric cancer IHC biomarkers, advanced detection methods, and perspectives https://pubmed.ncbi.nlm.nih.gov/42075555/ Digital Pathology Place https://digitalpathologyplace.com Digital Pathology 101 Free PDF book mentioned at the end of the episode through Digital Pathology Place. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #236
    May 15 · 1 hr 15 min

    236: What Happens When a Patient Sees Their Cancer for the First Time | Podcast with Michele Mitchell

    Send us Fan Mail What if the most frightening part of a pathology report is not the word cancer, but the silence that follows? In this episode of the Digital Pathology Podcast, Dr. Aleksandra Zuraw talks with Michele Mitchell—breast cancer survivor, caregiver, national patient advocate, and longtime volunteer across Michigan Medicine, ASCP, the Digital Pathology Association, and MyPathologyReport.ca—about what happened when she saw her own cancer slide years after treatment. That moment changed how she understood her disease, her risk, and her role as a patient advocate. This is not just a patient story. It is a digital pathology implementation story. The episode looks at how digital pathology removes practical barriers to sharing slides, why pathology clinics matter, and what becomes possible when pathologists move from being hidden in the background to becoming direct contributors to patient understanding. Michelle and Dr. Aleks talk through the communication gap around pathology reports, the emotional cost of delayed explanation, and the real-world workflow of pathology clinic visits built to help patients review their slides with the pathologist who made the diagnosis. They also discuss what the 21st Century Cures Act changed for patients, why immediate access to reports without interpretation can still create fear, and how pathology clinics can bridge the gap between raw data and real understanding. The conversation gets practical too: how patients can request a pathology clinic visit, what virtual pathology consults can look like, how billing and workflow concerns are already being addressed, and why the infrastructure question is smaller than many people assume. If you work in digital pathology, pathology informatics, patient communication, or implementation, this episode is a reminder that visibility is not extra. It is part of the value proposition. And for pathologists who worry this is too far outside the traditional role, the episode offers a grounded counterpoint: the workflows, templates, billing structures, and virtual options already exist. Highlights 00:00 – Why pathology needs to become more patient-centered Michele frames the core problem clearly: what often scares patients is not only cancer, but the silence around the diagnosis. 00:34 – How digital pathology changes the patient experience Digital slides make it possible for patients to see their diagnosis, compare normal and abnormal tissue, and ask better questions. 11:13 – What happened when Michele saw her cancer for the first time More than a decade after treatment, seeing her own slide changed how she understood her grade, her risk, and her daily health decisions. 16:19 – Why visual pathology can change adherence and lifestyle Michele explains how the image-based explanation became a practical turning point, not just an emotional one. 20:43 – The case for direct pathologist-patient communication The episode reviews why this can improve clarity, treatment understanding, clinic efficiency, and even professional satisfaction for pathologists. 38:40 – What a pathology clinic actually looks like From preparation and consent to slide review, plain language, empathy, and follow-up, the workflow is much more concrete than many people assume. 45:35 – ASCP’s certification workshop for pathology clinics Michele describes the national effort to make pathology clinics reproducible, scalable, and easier to implement. 49:32 – What the 21st Century Cures Act changed Patients now get near real-time access to reports, but that access still needs interpretation, context, and support. 01:03:23 – Pushback, logistics, and why the barriers are not where people think Time, reimbursement, scheduling, and virtual setup are addressed directly with examples already in practice. 01:16:57 – The future: patient-friendly reports, AI, and pathology as part of the care team The episode closes on a practical vision: not hype, but tools and workflows that already exist and can be connected now. Resources mentioned Digital Pathology Place – website and educational platform referenced by Dr. Aleks as the home for her work and resources. Digital Pathology 101 – Dr. Aleks’s book, referenced in the broader discussion of patient and pathologist education. Michigan Medicine breast pathology clinic – launched in 2023 as a patient-facing breast pathology clinic model. ASCP pathology clinic certification workshop – national workshop co-developed to help institutions build pathology clinics. 21st Century Cures Act – legal framework behind near real-time patient access to pathology reports and related health data. MyPathologyReport.ca – patient-friendly pathology education resource reviewed with patient advocate involvement. American Cancer Society Reach to Recovery – support resource mentioned for breast cancer patients. Scanslated – patient-friendly report interface discussed as part of a future-facing model for pathology communication. Virtual pathology consults/telehealth setup – discussed as a scalable way to lower implementation friction. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #235
    May 12 · 34 min

    235: From Cytology to Omics: Where Pathology AI Gets Harder

    Send us Fan Mail DigiPath Digest #45 asks a practical question: can AI in pathology move from correlation to real clinical use? In this episode, I review four papers that push on that question from different angles: computational pathology moving toward morphology-driven molecular inference, the current state of digital cytopathology and AI, multi-omics and precision oncology in hepatocellular carcinoma, and AI literacy in veterinary education. What ties them together is not model performance alone. It is the harder question of validation, workflow fit, quantitative use, ethics, and human oversight. In the first paper, I talk about computational pathology as more than pattern recognition. The focus is on morphology-driven molecular inference, digital biomarkers, and why spatial omics matters as biological ground truth. I also discuss why continuous quantitative scoring is more useful than forcing biology into rough scoring buckets. The second paper focuses on digital cytopathology. Cytology was early for FDA-cleared AI in cervical screening, but non-gynecologic cytology is still much harder to digitize because of specimen variability and workflow complexity. I also cover telecytology, rapid onsite evaluation, automation, and quality control. The third paper looks at hepatocellular carcinoma and AI-driven precision oncology. This part is about using AI and machine learning to integrate genomics, transcriptomics, proteomics, metabolomics, radiomics, and pathology to support biomarker discovery, tumor microenvironment analysis, and treatment stratification. The fourth paper may be the most broadly useful. It proposes an AI literacy curriculum for veterinary education that covers AI fundamentals, machine learning evaluation, LLMs, ethics, liability, and academic integrity. I think that matters far beyond veterinary medicine, because if clinicians are expected to use AI tools responsibly, AI literacy cannot stay optional. Highlights 00:01 Welcome and overview of the four papers 03:02 Computational pathology and morphology-driven molecular inference 11:01 Digital cytopathology, telecytology, and QC 20:47 AI/ML in hepatocellular carcinoma precision oncology 31:04 AI literacy in veterinary education 47:42 Final takeaways and Digital Pathology 101 update Resources Computational Pathology as a Mechanistic Discipline: From Morphology to Molecular Data https://pubmed.ncbi.nlm.nih.gov/42052846/ Advances in Digital Cytopathology and Artificial Intelligence Applications https://pubmed.ncbi.nlm.nih.gov/42046894/ Navigating the Labyrinth of Hepatocellular Carcinoma: Leveraging AI/ML for Precision Oncology https://pubmed.ncbi.nlm.nih.gov/42065059/ Curriculum Framework for Artificial Intelligence Literacy in Veterinary Education Front Vet Sci. 2026;13:1801756 Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #234
    April 25 · 37 min

    234: Quality, Teaching, and AI: A Practical Shift in Pathology

    Send us Fan Mail Where is AI in pathology actually becoming useful right now? In this episode of DigiPath Digest, I review 4 new PubMed papers across digital pathology, whole slide imaging (WSI), computational pathology, medical education, forensic pathology, and breast cancer AI. We look at a deep learning tool for coronary artery stenosis measurement in forensic autopsies, an AI-powered digital pathology model for renal pathology education, an open-source quality control tool for prostate biopsy whole slide images, and a breast cancer stage prediction model built for resource-constrained settings using low-magnification H&E slides. I also share updates on the upcoming second edition of Digital Pathology 101 and the decision to make AI paper summaries public on the podcast feed to help busy pathology professionals stay current. Highlights [01:28] Update on the upcoming second edition of Digital Pathology 101 and the release of public AI paper summaries for faster literature review. [05:22] Paper 1: Deep learning for coronary artery stenosis evaluation in forensic autopsies using whole slide imaging. Why objective stenosis measurement matters, how the model outperformed visual estimates, and why this could affect adoption in forensic pathology. [15:18] Paper 2: AI-powered digital pathology with case-based teaching in renal education. A practical discussion on annotated digital slides, flipped classroom learning, and how digital pathology can improve pathology education and diagnostic reasoning. [21:34] Paper 3: Open-source AI for quantitative quality control in prostate biopsy whole slide images. Why WSI quality control matters, what PathProfiler measures, and how automated QC can support remote pathology workflows. [32:38] Paper 4: Breast cancer stage prediction from H&E whole slide images in resource-constrained settings. A look at low-magnification AI, vision transformers, and what moderate performance can still mean when access to advanced testing is limited. [45:06] Closing thoughts, invitation to vote for future AI paper summaries, and a final reminder to download Digital Pathology 101. Resources Paper 1: Development of a deep learning-based tool for coronary artery stenosis evaluation in forensic autopsies using whole slide imaging PubMed: https://pubmed.ncbi.nlm.nih.gov/41998396/ Paper 2: Integrating AI-Powered Digital Pathology With Case-Based Teaching: A Novel Paradigm for Renal Education in Medical School PubMed: https://pubmed.ncbi.nlm.nih.gov/41995002/ Paper 3: Application of an open-source AI tool for quantitative quality control in whole slide images of prostate needle core biopsies - a retrospective study PubMed: https://pubmed.ncbi.nlm.nih.gov/41994924/ Paper 4: Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings PubMed: https://pubmed.ncbi.nlm.nih.gov/41993946/ Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #233
    April 24 · 22 min

    233: AI-Driven Breast Cancer Staging in Resource-Constrained Settings

    Send us Fan Mail Paper Discussed in this Episode: Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings. Bedőházi Z, Biricz A, Kilim O, et al. Journal of Pathology Informatics 21 (2026) 100644. Episode Summary: Welcome back, Trailblazers! In this Journal Club deep dive of the Digital Pathology Podcast, we flip the core assumption of microscopic precision on its head. Can an AI accurately predict pathological breast cancer stages (pTNM I-III) from a blurry, high-altitude 2.5x magnification snapshot? We explore a 2026 study that strips away standard high-resolution data to build a highly efficient, resource-aware AI diagnostic tool for clinics lacking supercomputers. We unpack the math, the models, and a haunting revelation about what primary tumors can tell us about distant metastasis. In This Episode, We Cover: • The Compute Bottleneck: Why the digital pathology AI revolution is leaving resource-constrained clinics behind, and how dropping from the standard 40x to 2.5x magnification slashes image patch extraction by 256 times, bypassing massive hardware and server requirements. • The "Airplane View": How the AI compensates for the loss of microscopic cellular details (like mitosis or cellular atypia) by relying on macroscopic features, identifying disease through overall tumor growth patterns and broad architectural disruption. • Vision Transformers & "Puzzle Bags": Why the UNI foundation model—a vision transformer fine-tuned on the BRACS dataset—outperforms older convolutional networks (like ResNet-50) by mapping long-range spatial dependencies across the entire image patch simultaneously. Plus, how Multiple Instance Learning (MIL) acts as a targeted "puzzle bag," mathematically weighting critical cancer data and ignoring irrelevant background noise. • The Real-World Stress Test: The model's solid performance on the internal Semmelweis dataset versus the massive external Nightingale cohort, where unsupervised data cleaning with t-SNE and DBSCAN clustering automatically deleted garbage data. We also discuss the AI's struggle with the TCGA-BRCA dataset due to severe domain shift from heterogeneous tissue preparation, specifically the structural tissue damage caused by frozen sections. • The "Messy Middle" and Clinical Triage: The model's tendency to struggle with Stage II breast cancer and the critical clinical danger of under-staging advanced Stage III cancers. We discuss why this WSI-only baseline isn't replacing human pathologists, but rather serves as an automated "sorting hat" for incomplete medical records or a highly tunable "smoke detector" to route suspicious slides for immediate manual review. Key Takeaway: The AI successfully predicted overall cancer stage—which inherently includes distant lymph node metastasis—by looking only at the primary tumor's architectural disruption, without ever evaluating a single lymph node slide. This proves that vital systemic biological secrets are hiding in plain sight in the macroscopic view of standard H&E slides, offering a phenomenal proof-of-concept for global health equity in resource-constrained settings Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #232
    April 22 · 18 min

    232: AI and Digital Pathology in Case-Based Renal Education

    Send us Fan Mail Paper Discussed in this Episode: Integrating AI-Powered Digital Pathology With Case-Based Teaching: A Novel Paradigm for Renal Education in Medical School. Zhou H, Cui L. Clin Teach 2026; 23(3):e70421. doi: 10.1111/tct.70421. Episode Summary: In this journal club episode tailored for healthcare trailblazers, we explore a massive paradigm shift in medical education. We examine a 2026 perspective article that uses the notoriously complex field of renal pathology as a stress test for a brand-new teaching model. Moving away from dark lecture halls and static, perfect images, we discuss what happens when artificial intelligence is actively combined with flipped classrooms, fundamentally redefining what it means to be a competent physician in the digital age. In This Episode, We Cover: • The "Bottleneck" of Renal Pathology: Why the kidney is the ultimate teaching hurdle. Students must translate the dense, flattened 2D reality of an H&E stain into an understanding of a patient's complex systemic autoimmune response. • The Danger of the "Curated Reality": Why traditional teaching methods that rely on textbook-perfect, heavily curated slides create "brittle" mental models. When students finally encounter messy, real-world biopsies with overlapping, ambiguous pathologies, the traditional educational foundation falls apart. • The "Spell Checker" for Histopathology: How collaborative AI elevates Whole Slide Imaging (WSI) beyond just high-resolution screens. The AI acts as a concurrent guide, using pixel-level pattern recognition to highlight regions of interest simultaneously and simulate the complex reasoning process of an expert pathologist. • The Case-Based Flipped Classroom (CBFC): The pedagogical engine that anchors these AI tools in clinical reality. Instead of passive lectures, students are handed the "detective's case file" beforehand to actively interrogate annotated slides, synthesizing diverse data streams to defend diagnoses in collaborative groups. • Redefining Medical Competence (The "Clinical Editor"): Why the new bottleneck in medical education isn't memorization—it's critical appraisal. We discuss the necessity of teaching "digital literacy," training students to skeptically manage AI, recognize its blind spots (like confusing a physical tissue fold for an abnormality), and actively audit the algorithm against the messy human reality of the patient. • The Impending Culture Collision: A look at the fascinating future where freshly minted, AI-native residents enter a legacy clinical workforce still transitioning away from physical glass slides, potentially reversing traditional medical hierarchies in the hospital. Key Takeaway: The goal of modern medical education is no longer just memorizing histological patterns, as that heavy lifting is being outsourced to algorithms. By fusing AI-powered digital pathology with the necessary friction of case-based learning, we are training a new generation of diagnosticians to view AI not as a crutch, but as a powerful collaborative tool that must be thoughtfully scrutinized and audited for safe patient care Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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  • #229
    April 20 · 22 min

    229: Spatial Omics and AI for Clinically Actionable Cancer Biomarkers

    Send us Fan Mail Paper Discussed in this Episode: Spatial omics and AI for clinically actionable cancer biomarkers. Reitsam NG. PLoS Med 2026; 23(4): e1005049. Episode Summary: In this deep dive, we explore how artificial intelligence and spatial omics are fundamentally rewriting the rules of cancer diagnostics. We break down a 2026 editorial that challenges a deceptively simple question driving modern oncology: Is a tumor "positive" or "negative" for a biomarker? As targeted cancer therapies evolve, this binary thinking is failing us. We discuss why mapping where and how much of a therapeutic target exists is crucial, and how AI is stepping in to solve the reproducibility issues human pathologists face when making borderline diagnostic calls. In This Episode, We Cover: • The Illusion of "Positive" vs. "Negative": Why the basic premise of modern cancer therapies—like antibody-drug conjugates (ADCs)—often falls apart in reality when we ignore the spatial heterogeneity of a tumor. • The Power of Computational Pathology: How AI is transforming subjective, qualitative estimates into continuous, reproducible data, scaling the quantification of complex biomarkers like PD-L1 and TROP2. • "Virtual" Proteomics: The fascinating concept of using AI models to infer high-dimensional spatial information and immune maps directly from standard, routine H&E stained slides. • The HER2 Bottleneck: A real-world look at the breast cancer drug T-DXd, which now demands pathologists distinguish between "HER2-low" and "HER2-ultralow". While human agreement drops below 70% at these fuzzy decision boundaries, AI steps up with a staggering ~97% sensitivity. • Three Shifts for the Future: Why clinical trials and routines must adopt continuous measures (like percentage of expressing cells), demand longitudinal repeat testing at disease progression, and utilize adaptive trial platforms. • Bridging the Gap to Reality: The massive hurdles preventing widespread adoption—such as equipment costs exceeding $250,000 and massive data storage needs. We discuss why a hybrid workflow that bolsters routine pathology with deployable AI is the best path forward to prevent widening global health disparities. Key Takeaway: The future of precision oncology isn't just about finding new drug targets; it’s about fundamentally changing how we measure them. By moving away from rigid binary thresholds and using AI to map the continuous, spatial reality of tumors, we can unlock the true potential of targeted therapies. However, achieving this diagnostic ecosystem requires overcoming significant financial and systemic hurdles—such as updating reimbursement pathways and proficiency testing—to ensure these life-saving insights are accessible across all healthcare settings. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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