Digital Pathology Podcast

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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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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:

1. Domain expertise is the prompt
2. Supply the sources
3. Encode corrections
4. 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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What good is a powerful foundation model if it slows the pathologist down, can’t explain its result, or doesn’t fit the clinical workflow?

Foundation models are gaining attention across digital pathology. But they’re not finished clinical tools by themselves.

In this episode of the Digital Pathology Podcast, I speak with Panu Kauppila, Chief Product Officer at Aiforia, about what foundation models are, how they differ from traditional convolutional neural networks, and what it takes to make them useful for pathologists.

Panu describes a foundation model as a large, context-aware building block. To perform a specific pathology task - such as grading, segmentation, or mitotic counting - it must be combined with an adapter, a task-specific head, curated annotations, a usable interface, and integration with the laboratory workflow.

We also discuss one of the biggest practical constraints: speed.

A pathologist shouldn’t have to click a button and wait for an analysis. Panu explains why AI should run automatically in the background so the results are already available when the case reaches the pathologist’s worklist.

The conversation also examines the tradeoff between model size, computational cost, and clinical performance. Larger models may improve robustness and generalizability, but they can also require more processing power. For clinical applications, Aiforia focuses on smaller and medium-sized foundation models that provide the necessary quality without making the workflow slower or unnecessarily expensive.

Annotated data remains central. The foundation model supplies the underlying image understanding, while the task-specific head and controlled annotations determine how the model performs on a particular pathology problem. This structure also raises important questions about bias, data provenance, ownership, regulatory documentation, and explainability.

Finally, we look at multimodal AI models that combine pathology images with reports, genomic data, molecular information, and clinical outcomes. These tools could support more interactive, predictive, and prognostic applications - but only if they’re introduced through secure, controlled workflows with clear audit trails.

Episode Highlights

• 00:00 — Where does bias enter a foundation model workflow?
  Panu distinguishes the underlying foundation model from the annotated dataset used to build the task-specific application.
• 00:27 — Meet Panu Kauppila
  An introduction to Aiforia’s Chief Product Officer and the episode’s focus on foundation models in digital pathology.
• 01:05 — From radiology AI to digital pathology
  Panu describes his background in medical device development, radiology, oncology solutions, and clinical AI implementation.
• 06:29 — Foundation models versus convolutional neural networks
  What makes foundation models more context-aware, robust, and generalizable across image datasets.
• 07:07 — Image-only and multimodal foundation models
  Why these two categories offer different capabilities and potential clinical uses.
• 07:50 — A foundation model is a platform, not a finished solution
  The underlying model may understand image features, but it still needs a task, interface, and clinical workflow.
• 08:34 — Foundation model, adapter, and task-specific head
  How these components work together to create an application for grading, mitotic counting, or another pathology task.
• 09:57 — The cost of larger models
  Why increased robustness must be balanced against computational demands, inference speed, and affordability.
• 11:12 — Pathologists won’t wait for AI
  Why even short delays can interrupt the clinical workflow.
• 11:46 — Running AI in the background
  A workflow in which slides are scanned, analyzed automatically, and added to the worklist with results ready for review.
• 12:16 — Combining foundation models with curated annotations
  How smaller task-specific datasets and adapter technology can produce practical pathology models.
• 15:24 — Generalizability across scanners, laboratories, and populations
  How foundation models may make adaptation to new domains more manageable.
• 16:36 — Two datasets, two sources of potential bias
  The regulatory questions created by an underlying foundation model and a separate controlled annotated dataset.
• 20:46 — Making foundation models accessible
  Why a platform and user interface are necessary for pathologists and researchers who don’t work directly with code.
• 21:57 — Testing foundation models in Aiforia Create
  How researchers can compare a CNN with supported foundation models in the same no-code environment.
• 25:45 — How foundation models are selected
  Quality, licensing, model size, annotated data, and the requirements of the intended use.
• 26:57 — Training cost versus inference cost
  Why a more expensive training iteration may still reduce total development costs if fewer iterations are needed.
• 32:23 — Pathologists are visual reviewers
  The importance of segmentation quality and showing exactly what the model identified.
• 36:26 — The potential of multimodal AI
  Combining pathology images with text, genomic information, molecular data, and clinical outcomes.
• 37:35 — Keeping multimodal AI inside a controlled environment
  Privacy, security, regulatory oversight, and the risks of moving clinical information into consumer AI tools.
• 43:24 — Explainability in clinical pathology AI
  Using semantic segmentation, object detection, instance segmentation, and annotated ground truth to show how results were calculated.
• 47:43 — Moving toward predictive and prognostic models
  How established digital workflows could allow pathologists to contribute more information about likely outcomes.
• 49:45 — Research and clinical collaboration with Aiforia
  How interested researchers and laboratories can connect through the Aiforia website.

Resources Mentioned

• Aiforia
• Aiforia Create no-code model-development environment
• PathChat

Listen to the full discussion to understand what foundation models can add to digital pathology—and what still has to happen before they become practical, trusted clinical tools.

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Does more experience automatically make a cytotechnologist more accurate—or does where they look first matter more?

In DigiPath Digest #50, I review a digital cytology eye-tracking study that challenges the assumption that diagnostic accuracy improves steadily with years of practice.

The researchers tracked the visual behavior of 100 board-certified cytotechnologists with 1 to 40 years of experience. They found no statistically significant linear relationship between years of experience and diagnostic accuracy. Instead, low-power field efficiency—the ability to identify an important target quickly within a wider field—emerged as the key predictor of high accuracy discussed in the study.

The study also examined whether this visual skill can be developed. Twenty-eight students completed an intensive three-month cytotechnology training program. After training, they located diagnostic targets more quickly and spent less attention on normal, nondiagnostic cells. In other words, they learned both where to look and what to disregard.

What could this mean for digital pathology education?

As AI-assisted workflows take on more of the exhaustive searching, cytotechnologists and pathologists may increasingly work as expert verifiers. That requires rapid target assessment, strong knowledge of normal morphology, and awareness of risks such as confirmation bias and cognitive fatigue.

The study has an important limitation: it used static images rather than dynamic whole slide imaging. The findings raise useful questions about visual expertise, training, and competency assessment, but they shouldn’t be generalized beyond the study design without further research.

Episode Highlights

• 00:00 – Welcome to DigiPath Digest #50 and introduction to the paper
• 04:10 – Why the traditional definition of professional expertise is changing
• 07:02 – Moving from exhaustive searching to verification in AI-assisted workflows
• 09:04 – How eye tracking was used with 100 board-certified professionals
• 10:25 – Years of experience versus diagnostic accuracy
• 13:13 – Experience-based caution and attention to sample information
• 15:16 – Low-power field efficiency as a predictor of high accuracy
• 17:05 – Practical low-power field demonstration using a whole slide image
• 20:15 – Searching versus detecting and the mental map of normal morphology
• 24:04 – Comparing high- and low-performer visual scan paths
• 25:27 – Cognitive filtering: knowing what not to examine
• 27:35 – Can visual efficiency be taught in three months?
• 29:55 – How AI may shift the human role from searcher to verifier
• 30:38 – Study limitations: static images versus dynamic whole slide imaging
• 32:37 – Could gaze efficiency influence future competency assessment?
• 33:44 – Digital pathology learning resources and closing thoughts

Resources Mentioned

• Abstract and paper: Screening Efficiency Over Experience: Rapid Target Detection in Low-Power Field as a Modifiable Cognitive Biomarker for Diagnostic Accuracy in Digital Cytology

Listen to the full DigiPath Digest #50 recording to examine what the study found, what it didn’t prove, and how visual search skills could influence digital cytology training.

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Can a treatment decision depend on whether one pathologist sees 45% biomarker positivity and another sees 55%?

Visual immunohistochemistry scoring helped establish precision oncology. But as targeted therapies become more sensitive to subtle biological differences, categorical scores such as 0, 1+, 2+, and 3+ may no longer capture the information needed to identify the right patients.

In this episode, I speak with three Roche experts:

• Gordana Juric-Sekhar, MD, anatomic pathologist
• Saleh Miri, PhD, Director of Digital Pathology AI Algorithms
• Purvi Gaglani, Regulatory Affairs Lead for Digital Pathology

We discuss how computational pathology is changing companion diagnostics by moving biomarker assessment from visual estimates to continuous, cell-level measurements.

The conversation examines the limitations of manual IHC scoring, including interobserver variability, intraobserver variability, visual fatigue, borderline cases, tumor heterogeneity, and the inability of the human eye to measure complex spatial relationships.

Using TROP2 scoring in advanced non-small cell lung cancer as an example, Saleh explains the normalized membrane ratio. This computational metric measures protein expression at the cell membrane relative to total expression within the cell—something that can’t be reproduced through conventional visual scoring.

We also clarify the difference between computer-assisted scoring and a fully computational companion diagnostic. An assisted tool supports a pathologist’s visual interpretation. A computational CDx generates the biomarker measurement through algorithmic, cell-level analysis.

That doesn’t remove the pathologist.

Pathologists remain responsible for evaluating tissue quality, staining quality, scan quality, tumor selection, image analysis results, and the final clinical context. They can reject a stain, request a rescan, exclude inappropriate regions, question the result, or seek a second opinion.

The episode also examines the regulatory implications of computational companion diagnostics. Instead of evaluating a single IHC assay, regulators may need to assess the complete system - from tissue preparation and staining to scanning, image management, algorithmic analysis, display, and the final biomarker report.

Finally, we discuss what laboratories will need to implement these workflows, including validated scanning infrastructure, cybersecurity, tighter preanalytical process control, and training that helps pathologists interpret continuous computational measurements.

Episode Highlights

• 00:00 — Pathologists remain central to computational CDx
  Why computational tools provide more precise measurements without replacing pathology expertise.
• 01:09 — Why companion diagnostics are changing
  Visual IHC scoring helped launch precision oncology, but the model is approaching its limits.
• 04:53 — The current companion diagnostic landscape
  How IHC, next-generation sequencing, liquid biopsy, and visual biomarker scoring are used today.
• 07:01 — The mathematical burden placed on the human eye
  Why manually assessing tens of thousands of tumor cells requires pathologists to estimate rather than calculate.
• 08:39 — The borderline patient dilemma
  A digital tool can distinguish measurements such as 74% and 76%, while that difference is difficult to reproduce visually.
• 09:27 — Why spatial context matters
  Computational pathology can measure biomarker heterogeneity, clustering, and relationships between tumor and immune cells.
• 12:17 — Where manual scoring reaches its limits
  Interobserver variability, intraobserver variability, fatigue, staining interpretation, and heterogeneous tumors.
• 18:29 — Moving from judgment calls to quantified measurements
  Why the next stage of precision oncology requires information beyond human visual perception.
• 19:14 — Computer-assisted scoring versus computational CDx
  The important distinction between helping a pathologist calculate an existing score and generating a new algorithmic measurement.
• 23:22 — Computational pathology and decentralized workflows
  How digital images can support remote review, access to expertise, and second opinions.
• 27:48 — Why therapies require higher-resolution biomarkers
  Modern targeted treatments may respond to biological differences that categorical scoring can’t capture.
• 32:09 — TROP2 in advanced non-small cell lung cancer
  The episode’s example of a biomarker requiring computational measurement.
• 33:26 — Understanding the normalized membrane ratio
  How the algorithm measures membrane expression relative to total protein expression at the individual-cell level.
• 35:46 — Working with regulators on a new diagnostic model
  Purvi discusses global health authority engagement and the FDA Breakthrough Device Designation.
• 38:33 — The computational CDx as a system of systems
  Why staining, scanning, image management, algorithms, displays, and reporting must be evaluated together.
• 40:11 — Changes to validated workflow components
  How using a different scanner, monitor, or other component could fall outside the defined device configuration.
• 43:30 — Why computational pathology is becoming necessary
  Continuous measurements can reveal biomarker-treatment relationships that may remain hidden within categorical scores.
• 49:12 — The pathologist’s role in the workflow
  Reviewing sample, staining, scan, image, algorithmic analysis, and the final biomarker result.
• 53:39 — Digital second opinions
  How image management systems can simplify collaboration without physically transporting glass slides.
• 56:43 — What laboratories need to prepare
  Validated infrastructure, cybersecurity, preanalytical control, training, and digital pathology literacy.
• 58:49 — Learning to interpret computational results
  The shift from visually estimated categories to continuous, quantitative biomarker measurements.

Resources Mentioned

• Full discussion on YouTube: https://youtu.be/oKW1xC6TTZg

Listen to the full discussion to understand how computational pathology could change companion diagnostics—and what pathologists, laboratories, and regulators must prepare for next.

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Is your digital pathology rollout moving so slowly that it’s creating a fragmented workflow instead of transforming the department?

In this episode of the Digital Pathology Podcast, I speak with Dr. Syed Hoda, Director of Digital Pathology at NYU, about why gradual implementation may no longer be the best approach to digital pathology adoption.

Dr. Hoda explains how NYU used an intensive nine-month planning period to prepare for a department-wide transition. The process involved pathology, IT, project managers, vendors, hospital leadership, and approximately 40–50 people participating in regular planning calls.

This wasn’t simply a scanner installation.

The team mapped workflows, configured Epic Beaker, redesigned laboratory spaces, tested integrations, planned training, and addressed the practical concerns of nearly 100 pathologists.

We also discuss why scanner specifications may matter less than integration, vendor support, training, and system performance. For Dr. Hoda, digital pathology had to work as smoothly as glass microscopy. Speed was non-negotiable.

Change management played an equally important role. Through open discussions, town halls, and the ADKAR framework, the team addressed concerns ranging from ergonomics to the loss of collaborative microscope sessions.

The result? Every pathologist adopted the digital workflow, no one left the department because of the transition, and approximately 60–65 pathologists now work remotely using equipment that matches their office setup.

Finally, we examine the next step: artificial intelligence in pathology. Dr. Hoda explains why NYU focused on building a reliable digital foundation before introducing AI. He also raises important questions about validation, transparency, responsibility, regulatory clearance, and the need for greater pathologist involvement in AI development.

Episode Highlights

• 00:00 — Are we repeating the same mistakes with pathology AI?
  Dr. Hoda compares the current excitement around AI with the early promises made about digital pathology 15 years ago.
• 01:04 — Meet Dr. Syed Hoda
  His clinical pathology background and path to becoming NYU’s Director of Digital Pathology.
• 03:16 — Why going slowly can hold departments back
  How partial adoption creates fragmented workflows, inconsistent training, and prolonged implementation.
• 06:25 — Leadership support for rapid adoption
  Why institutional commitment, resources, and an ambitious timeline made the project possible.
• 10:13 — Nine months of detailed planning
  Workflow mapping, laboratory changes, system configuration, vendor selection, testing, and validation.
• 11:48 — The role of professional project management
  Why pathologists shouldn’t be expected to coordinate every part of a complex digital transformation.
• 14:29 — Why the scanner isn’t the most important decision
  Image quality matters, but integration, service, training, and workflow fit may matter more.
• 17:42 — People matter more than machines
  How vendor relationships and departmental engagement supported adoption.
• 19:19 — Setting clear expectations across the department
  NYU communicated that every pathologist would move to digital sign-out within a defined period.
• 20:49 — Change management is a structured process
  How the ADKAR framework guided communication, education, adoption, and reinforcement.
• 25:07 — Addressing practical and personal concerns
  From mouse ergonomics to preserving collaborative case review between pathologists.
• 27:19 — Why NYU didn’t introduce AI first
  Dr. Hoda explains why pathologists needed to become comfortable with the digital platform before adding new AI tools.
• 29:26 — Digital pathology and remote sign-out
  Approximately 60–65 pathologists now work remotely with equipment matching their office setup.
• 30:28 — Why speed is non-negotiable
  Even a small delay or repeated pixelation can quickly undermine confidence in a digital workflow.
• 33:25 — A cautious approach to pathology AI
  Concerns about premature adoption, self-validation, limited regulatory clearance, and lack of pathologist involvement.
• 37:27 — Scientific validation, transparency, and responsibility
  What happens when the AI result and the pathologist’s interpretation don’t agree?
• 40:41 — Where AI could meaningfully augment pathology
  Quantifying microenvironments, feature combinations, ratios, and findings that are difficult to assess visually.

Resources Mentioned

• ADKAR change management framework
• Digital Pathology Association
• Executive War College
• FDA list of AI-powered medical devices
• A radiology mock-trial paper examining responsibility when clinicians use AI: Examining perceptions of liability about AI in radiology (MedRxiv)
• Why AI cannot do good science without humans (Nature Editorial)
• A previous Digital Pathology Podcast discussion about AI-supported colorectal cancer feature analysis (How to use deep learning image analysis for colon cancer with Rish Pai)

Listen to the full conversation for a practical look at digital pathology planning, change management, remote sign-out, scanner integration, and responsible AI adoption.

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