Digital Pathology Podcast

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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 year
What makes Grundium's new Ocus R different: independent scanning heads you add over time, a slide tray instead of a door
Why the biggest whole slide scanner isn't always the right one for your lab's slide volume
How AxLab's robotic arm feeds slides into a scanner, and why robotics is only now reaching pathology
Why Philips' DICOM-ready scanner files matter for storage and cloud AI
Why 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 Proscia
Where virtual staining is ready, and where it still needs validation for every marker
Elsie, the humanoid lab assistant from Tactus AI
Who 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

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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 diagnosis
How long it takes to scan a slide, and why the 15 x 15 mm benchmark means little to a pathologist
How a whole slide scanner turns a glass slide into a digital image
Whether AI will replace pathologists
Why a digital slide is a data source, not a data point
The parts of the digital pathology workflow, from pre-analytics to AI
What 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

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

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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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