Digital Pathology Podcast

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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 holes
Stephanie Fullerton introduces two new members of the Hamamatsu team
Jessica 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 upgrade
Gary 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

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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.
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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 pathology
Domain Generalization of Histopathology Foundation Models in Multicenter, Multi-Scanner Cohorts: A Comparative Benchmark
Slide Selection Introduces Sampling-Induced Prediction Uncertainty in Digital Pathology AI: Evidence from Multi-Slide Breast Cancer Cohorts
Data resource mentioned:
The Cancer Genome Atlas (TCGA) through the Genomic Data Commons (GDC) portal
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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

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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
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