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Send us Fan Mail Robots are reaching the pathology lab, whole slide scanners are back in the spotlight, and pathology AI companies are getting bought. Dr. Aleks Zuraw, board-certified veterinary pathologist and host of the Digital Pathology Podcast, recaps PathVisions 2026, the Digital Pathology Association's annual meeting, with Trang Vu, who comes from the spatial biology side and tracks digital pathology vendors closely. You'll learn: Why hardware and robotics took over the PathVisions exhibit hall this yearWhat makes Grundium's new Ocus R different: independent scanning heads you add over time, a slide tray instead of a doorWhy the biggest whole slide scanner isn't always the right one for your lab's slide volumeHow AxLab's robotic arm feeds slides into a scanner, and why robotics is only now reaching pathologyWhy Philips' DICOM-ready scanner files matter for storage and cloud AIWhy standalone pathology AI companies are partnering with image management systems (IMS)What this year's deals mean: AstraZeneca and Modella, Roche and PathAI, Agilent and ProsciaWhere virtual staining is ready, and where it still needs validation for every markerElsie, the humanoid lab assistant from Tactus AIWho small and remote scanners are for, including a $4,500 option for rural hospitals Chapters 00:00 Elsie: a robot that reloads your scanner overnight 01:05 PathVisions 2026 in 30 seconds 01:42 Meet Trang Vu 03:01 Why PathVisions is "USCAP for the cool kids" 05:03 Hardware is back on the exhibit floor 07:18 Grundium's Ocus R: launch strategy and design 13:17 Do you need a 600-slide scanner? 14:21 AxLab's robotic arm 15:29 Why robots are only now reaching pathology 18:03 Philips and Evident unveil new scanners 18:55 Why pre-show buzz beats a booth unveiling 20:52 Philips: DICOM-ready, smaller whole slide image files 23:20 Pathology AI companies and image management systems 27:55 Modella and AstraZeneca 30:27 "Acquisition row": PathAI, Roche and Proscia 32:26 Agilent's full digital pathology stack 35:16 Virtual staining: where it's ready and where it isn't 39:51 Elsie, the humanoid lab assistant from Tactus AI 44:10 Direct-to-digital imaging and why digitization pays off 47:54 Small and remote whole slide scanners 51:08 A $4,500 remote scanner for rural hospitals 54:00 What's next: USCAP 2027 in Vancouver Good to know: Agilent has signed a definitive agreement to acquire Proscia; the deal hasn't closed yet. Philips' "50% smaller files" is Philips' own up-to figure based on preliminary data. Follow Trang Vu on LinkedIn: https://www.linkedin.com/in/trang-nguyen-vu/  Dr. Beth Plocharczyk's episode on serving two community hospitals with remote scanners:  https://digitalpathologyplace.com/podcast/digital-pathology-for-community-hospitals-dr-elizabeth-plocharczyk/ Free Digital Pathology 101 book: https://www.aleksandrazuraw.com/book Paid partnership with Hamamatsu. #DigitalPathology #PathVisions2026 #WholeSlideImaging #LabAutomation #PathologyAI Support the show Get the "Digital Pathology 101" FREE E-book and join us!

Send us Fan Mail Is a digital slide as good as glass? Live from the Hamamatsu booth at PathVisions 2026, Dr. Aleks Zuraw and Amy Welch, Product Marketing Manager for NanoZoomer at Hamamatsu, open the question box and answer the beginner questions attendees dropped in all week. Elizabeth Blakely joins as a surprise guest. You'll learn: Whether a digital slide is as good as glass for diagnosisHow long it takes to scan a slide, and why the 15 x 15 mm benchmark means little to a pathologistHow a whole slide scanner turns a glass slide into a digital imageWhether AI will replace pathologistsWhy a digital slide is a data source, not a data pointThe parts of the digital pathology workflow, from pre-analytics to AIWhat to learn first if you're new to the field Chapters 00:00 Live from PathVisions 2026 with Amy Welch 00:56 Is a digital slide as good as glass? 02:49 What the NanoZoomer numbers mean 03:22 How long does it take to scan a slide? 05:14 What surprised us most when we started in digital pathology 09:29 Is there a scanner for small volumes? 10:15 Will AI replace pathologists? 16:42 Pathology clinics: patients seeing their own slides 17:44 How does a whole slide scanner work? 21:11 What is digital pathology? 25:14 Digital pathology in 25 years 26:07 Surprise guest: Elizabeth Blakely 29:33 Direct-to-digital tissue imaging 32:22 The parts of the digital pathology workflow 35:03 What should someone new to digital pathology learn first? 38:09 Why "15 x 15 mm in 30 seconds" means nothing to a pathologist 39:46 Where to learn more: Digital Pathology 101 In partnership with Hamamatsu. Free Digital Pathology 101 book: https://www.aleksandrazuraw.com/book #DigitalPathology #PathVisions2026 #WholeSlideImaging #NanoZoomer #PathologyAI Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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!

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!

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!

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!

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!

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!

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!

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!

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 frameworkDigital Pathology AssociationExecutive War CollegeFDA list of AI-powered medical devicesA 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. 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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 PodcastMIT 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!

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 ScienceCandice’s earlier work on ChatGPT in veterinary medicineTexas A&M and the institutional setting where Candice is building AI research and teachingMr. Don Riddick and the AVMA AI working group, mentioned in the ethics and legal contextClaude, Codex, and Perplexity as AI tools Candice is actively testingDigital Pathology 101, mentioned in the conversation as a teaching resourceCandice’s online educational work on Instagram. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

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/bioengineering13050577Review article / journal landing page: https://doi.org/10.3390/bioengineering13050577Benchmarks 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/bioengineering13050577PathBench — public benchmark paper: https://arxiv.org/abs/2505.20202MEDFAIR — benchmark paper: https://arxiv.org/abs/2210.01725MEDFAIR code repository: https://github.com/ys-zong/MEDFAIRModels mentioned:Model overview in the review (Virchow/Virchow2, UNI, CONCH, H-Optimus, GigaPath, TITAN, Mayo Clinic Atlas): https://doi.org/10.3390/bioengineering13050577Virchow: https://arxiv.org/abs/2309.07778UNI: https://arxiv.org/abs/2308.15474CONCH: https://arxiv.org/abs/2307.12914Mayo Clinic Atlas: https://arxiv.org/abs/2501.05409TITAN: https://arxiv.org/abs/2411.19666Dataset 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!

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 – [email protected] 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!