Digital Pathology Podcast

Aleksandra Zuraw, DVM, PhD
  • 5.0 (8)
  • Natural Sciences
  • Weekly

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

  1. 1d ago ·  Video

    253: What Happens After Your Whole Slide Scanner Arrives? | PathVisions 2026

    Send us Fan Mail What happens after your whole slide scanner arrives in the lab? Live from the Hamamatsu booth at PathVisions 2026, Dr. Aleks Zuraw meets the people who help labs go digital, plus a surprise guest from the DICOM standard. In this episode: David Clunie of DICOM Working Group 26 on the PathVisions 2026 DICOM Connectathon: scanners, archives, viewers and annotation tools from different vendors working together, and the new work on annotations with holesStephanie Fullerton introduces two new members of the Hamamatsu teamJessica Calafati, NanoZoomer national sales manager, on why going digital is easier than it was 10 years ago, what to do about a high rescan rate, and why your scanner and image management system (IMS) vendors need to talk before every upgradeGary Davis, business development manager for MoxiePlex, on multiplex fluorescence imaging and spectral unmixing Chapters 00:00 What happens after your whole slide scanner arrives 00:26 Live from the Hamamatsu booth at PathVisions 2026 01:07 David Clunie on the DICOM Connectathon results 02:48 DICOM annotations: contours within contours 05:06 Hamamatsu's new team members 06:09 Jessica Calafati: why going digital is easier now 07:47 How to fix a high rescan rate 09:29 Why scanner and software teams need to talk 09:59 Gary Davis on MoxiePlex 11:18 Spectral unmixing in three sentences 12:41 Send us your scanner questions Free Digital Pathology 101 book: https://www.aleksandrazuraw.com/book In partnership with Hamamatsu. #DigitalPathology #WholeSlideImaging #DICOM Support the show Get the "Digital Pathology 101" FREE E-book and join us!

  2. Oct 18

    252: Digital Pathology Trends 2026: Agilent Buys Proscia | PathVisions

    Send us Fan Mail What are the biggest digital pathology trends of 2026, and what does Agilent buying Proscia tell us? It's the clearest sign yet that the field is moving from shiny algorithms to full, connected workflows. In this live recap from Pathology Visions 2026 in San Diego, Dr. Aleks Zuraw (DVM, PhD, DACVP), board-certified veterinary pathologist and host of the Digital Pathology Podcast, sits down with Imogen Fitt of Signify Research to break down the biggest news of the week and the trends behind it. Imogen tracks the digital pathology market for a living. Aleks brings the pathologist's view. Together they cover the Agilent and Proscia deal, why software is finally being built around pathologist ergonomics, how scanner differences can change AI results, and whether every AI tool needs FDA clearance or can live as a lab-developed test (LDT). Key takeaways - Agilent's acquisition of Proscia is the peak of a partnership trend: everyone wants to be compatible with everyone. - This year's software news centers on ergonomics, voice dictation and voice AI, not just new algorithms. - PathVisions posters compared scanners and showed that image differences between models can change AI output. Purpose and intended use have to be defined within a whole system. - "Good enough" can be the right specification when AI is a safety net that flags cases for a human. - Not everything needs to be a medical device. The LDT pathway lets single labs start, gather data, and decide later whether to pursue clearance. - Pay-per-scan models and implementation work, not hype, are what will get digital pathology to more labs. Episode Highlights 00:36 - Agilent acquires Proscia - The biggest digital pathology news heading into PathVisions. 01:10 - Aperture and Concentriq - AI that finds clinical trial patients and an AI-enabled image management system. 01:43 - Ergonomics and voice AI - Why nobody likes pop-up windows, and what's replacing them. 02:16 - From staining to sign-out - Aleks recalls first seeing Agilent's full workflow at USCAP. 03:39 Scanners change AI output - What this year's posters showed, and why it surprised a pathologist. 05:09 - Good enough vs perfect - Imogen plays devil's advocate on accuracy targets. 07:15 - The pixel pathway - How locked systems ended up driving interoperability. 08:02 - FDA clearance or LDT? - Does every pathology AI tool need to be a medical device? 11:03 - Implementation over hype - Why the "boring" topics are the real progress. 12:03 - Pay-per-scan - Getting digital pathology to labs that can't buy a scanner upfront. Resources Mentioned - Signify Research: https://www.signifyresearch.net/ - Pathology Visions 2026: https://digitalpathologyassociation.org/pathology-visions-conference - Free Digital Pathology 101 book: https://www.aleksandrazuraw.com/book Connect with Imogen Fitt on LinkedIn: https://www.linkedin.com/in/imogen-fitt-194566127/ Enjoyed this episode? Share it with one colleague who's still deciding whether to go digital. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

  3. Oct 15

    251: Pathology Foundation Models: PathBench, Scanner Shift, and the HER2 Slide Swing

    Send us Fan Mail What if changing the scanner or choosing another slide from the same tumor changes your pathology AI results? In DigiPath Digest #52, I review three studies on pathology foundation models, scanner shift, and slide selection to ask how much of “model performance” comes from the model itself. We look at a benchmark comparing 32 foundation models across 41 tasks, a study testing 10 models across scanners and centers, and a breast cancer study examining how predictions change when different slides represent the same patient. The findings challenge familiar assumptions about bigger models, benchmark rankings, and the idea that one slide can represent a heterogeneous tumor. Combining information from multiple slides improved performance in the breast cancer study, without changing the model architecture. My takeaway: evaluate the model, the data, and the workflow together. I share three practical questions to ask before accepting an AI benchmark score. Highlights with Timestamps 00:42 — PathVisions updates and plans for conference activities.05:09 — Trailblazer podcast interviews: sharing real implementation experience.08:02 — How much of foundation model performance comes from the model?13:16 — The 32-model benchmark and closely grouped leading scores.16:06 — Why bigger models do not consistently perform better on unfamiliar data.18:58 — Scanner shift and testing models on scanners held out during training.22:18 — Why AUROC alone can miss important performance losses.24:55 — Use-case-specific validation and the “pixel pathway” from scanning to diagnosis.28:37 — Slide selection, tumor heterogeneity, and HER2 prediction.33:46 — Combining slides from the same patient: performance gains and computational cost.35:31 — Three questions to ask before trusting a benchmark.37:27 — Audience Q&A on available datasets and checking AI-generated answers.40:39 — Plans for the Digital Pathology Club and more accessible expert guidance.Resource from this episode Research papers: A benchmark study of vision and pathology foundation models for computational pathologyDomain Generalization of Histopathology Foundation Models in Multicenter, Multi-Scanner Cohorts: A Comparative BenchmarkSlide Selection Introduces Sampling-Induced Prediction Uncertainty in Digital Pathology AI: Evidence from Multi-Slide Breast Cancer CohortsData resource mentioned: The Cancer Genome Atlas (TCGA) through the Genomic Data Commons (GDC) portalSupport the show Get the "Digital Pathology 101" FREE E-book and join us!

  4. Oct 8

    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 promptSupply the sourcesEncode correctionsThe subject matter expert verifiesIn this episode: What the LLM does vs. what the pathologist does, task by taskWhat Toxicologic Pathology, Veterinary Pathology and Elsevier journals require when you disclose AI usePathology LLM tools already working on tox study reports, image analysis and whole slide imagesThe reference I didn't check that almost cost me a publicationWhy an LLM will happily justify switching patients from Tylenol to acetaminophenAutomation bias, model bias, and how to start with the tools your institution has approvedGoing 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!

  5. Aug 31

    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 time01:40 - How two image models performed worse than a coin flip on cell clusters02:29 - Image models, language models, and vision-language models04:40 - Why AI adoption in cytopathology remains low05:25 - Scattered single cells versus hyperchromatic crowded cell groups08:14 - AUCs fall from 0.950-0.996 to 0.385-0.68310:02 - How ResNet-50 and GoogLeNet failed differently11:04 - What the attention maps revealed13:22 - The intended-use lesson for pathology AI17:02 - Current applications of large language models in cytopathology18:39 - Structured reporting and the 99.4% accuracy result19:32 - Diagnostic support, AMIE, and the top-10 limitation21:09 - Quality-control applications and the missing false-positive rate23:30 - Why cytopathology still needs domain-specific language models24:54 - Retrieval-augmented generation, education, and research support27:07 - Two AI families, one requirement: direct validation28:35 - Context of use and intended-use validation29:12 - Human-reviewed training data and destructive book scanning32:02 - FDA and European approaches to evolving AI models35:00 - Protecting patient and practitioner well-being36:21 - Cytopathology-specific benchmarks and shared test sets38:16 - Why low-risk AI applications should come first39:50 - Cytopathology’s direct-to-digital advantage40:39 - Digital Pathology 101 and Pathology VisionsResources from this episode Watch DigiPath Digest #51Diagnostic performance of AI models trained on scattered single-cell imagesLeveraging large language models to enhance cytopathologyFDA guidance on AI and context of useFDA guidance on predetermined change control plansGoogle Research: AMIE diagnostic medical AIEU In Vitro Diagnostic Medical Devices RegulationEU Artificial Intelligence ActDigital Pathology 101Pathology Visions 2026Support the show Get the "Digital Pathology 101" FREE E-book and join us!

  6. Aug 20

    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 AiforiaAiforia Create no-code model-development environmentPathChatListen 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!

  7. Aug 17

    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 paper04:10 – Why the traditional definition of professional expertise is changing07:02 – Moving from exhaustive searching to verification in AI-assisted workflows09:04 – How eye tracking was used with 100 board-certified professionals10:25 – Years of experience versus diagnostic accuracy13:13 – Experience-based caution and attention to sample information15:16 – Low-power field efficiency as a predictor of high accuracy17:05 – Practical low-power field demonstration using a whole slide image20:15 – Searching versus detecting and the mental map of normal morphology24:04 – Comparing high- and low-performer visual scan paths25:27 – Cognitive filtering: knowing what not to examine27:35 – Can visual efficiency be taught in three months?29:55 – How AI may shift the human role from searcher to verifier30:38 – Study limitations: static images versus dynamic whole slide imaging32:37 – Could gaze efficiency influence future competency assessment?33:44 – Digital pathology learning resources and closing thoughtsResources 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 CytologyListen 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!

  8. Aug 13

    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 pathologistSaleh Miri, PhD, Director of Digital Pathology AI AlgorithmsPurvi Gaglani, Regulatory Affairs Lead for Digital PathologyWe 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/oKW1xC6TTZgListen 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!

See All (253)

Trailer

5
out of 5
8 Ratings

About

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.

Information

  • Creator
    Aleksandra Zuraw, DVM, PhD
  • Years Active
    2019 - 2026
  • Episodes
    253
  • Rating
    Explicit
  • Copyright
    © 2026 Digital Pathology Podcast
  • Show Website
    Digital Pathology Podcast

You Might Also Like