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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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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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 Podcast
• MIT 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.

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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 Science
• Candice’s earlier work on ChatGPT in veterinary medicine
• Texas A&M and the institutional setting where Candice is building AI research and teaching
• Mr. Don Riddick and the AVMA AI working group, mentioned in the ethics and legal context
• Claude, Codex, and Perplexity as AI tools Candice is actively testing
• Digital Pathology 101, mentioned in the conversation as a teaching resource
• Candice’s online educational work on Instagram.

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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/bioengineering13050577
• Review article / journal landing page:
https://doi.org/10.3390/bioengineering13050577
• Benchmarks 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/bioengineering13050577
• PathBench — public benchmark paper:
https://arxiv.org/abs/2505.20202
• MEDFAIR — benchmark paper:
https://arxiv.org/abs/2210.01725
• MEDFAIR code repository:
https://github.com/ys-zong/MEDFAIR

• Models mentioned:

• Model overview in the review (Virchow/Virchow2, UNI, CONCH, H-Optimus, GigaPath, TITAN, Mayo Clinic Atlas):
https://doi.org/10.3390/bioengineering13050577
• Virchow:
https://arxiv.org/abs/2309.07778
• UNI:
https://arxiv.org/abs/2308.15474
• CONCH:
https://arxiv.org/abs/2307.12914
• Mayo Clinic Atlas:
https://arxiv.org/abs/2501.05409
• TITAN:
https://arxiv.org/abs/2411.19666

• Dataset 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/

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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 – info@indicalab.com
• HALO 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.

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Can AI copilots really keep up with pathologists when the cases are new, the workflow is messy, and the benchmark is actually protected from leakage?

In this episode of DigiPath Digest #48, I focus on one paper: DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset. I chose this paper because I think the field needs more of this kind of work. Less hype. More evaluation. Less “look what AI can do.” More “how do we test it in a way that actually means something?”

In this session, I look at what makes DALPHIN important for pathologists, lab leaders, and digital pathology trailblazers trying to make sense of pathology AI right now. The paper benchmarks three models against human pathologists: two general-purpose models, Gemini 2.5 Pro and GPT-5, and one pathology-specific model, PathChat+. The dataset includes 1,236 images from 300 cases, covering 130 diagnoses, 14 pathology subspecialties, and cases from six countries. Human performance is benchmarked with 31 pathologists from 10 countries.

What I like about this paper is that it does not stop at top-line performance. It deals with the benchmarking problem itself. The authors built a sequestered, indirectly accessible ground truth so the evaluation data could not simply be scraped into model training. That matters because without that protection, benchmarking can become an illusion of genius rather than a real test of generalization.

The results are interesting and more nuanced than a simple win-or-lose story. PathChat+ reached expert-level performance in four of six tasks, Gemini in two of six, and GPT in one of six. That tells us something important already: pathology-specific training matters. But it also does not mean pathology is solved. In organ recognition, expert pathologists still outperformed all the models. In rare cancers, none of the models reached expert-level performance. And in ambiguous cases, the models still struggled with something human pathologists do all the time: expressing uncertainty.

I also spend time on one of the most practical parts of the paper: model behavior. Gemini tended to overcall. GPT tended to undercall. PathChat was more balanced. That matters in practice. A pathologist using a copilot needs to know the tool’s calibration bias before they can safely interpret what it is telling them. I also talk about anchoring bias in conversational interfaces, where early hallucinations can propagate through later answers if memory is not reset between questions. That is not just a technical curiosity. That is a workflow and safety issue.

Why should you listen? Because this episode is really about a bigger question: What kind of evidence should pathologists demand before AI copilots enter real workflows? If you want to understand validation, data leakage, rare-case performance, uncertainty, and why these tools should still be treated as co-pilots rather than autopilots, this is a useful paper to know.

Episode Highlights

01:20 – Why I chose the DALPHIN preprint and why benchmarking matters right now.

05:38 – What is in the DALPHIN dataset: 300 cases, 130 diagnoses, 14 subspecialties, 6 countries.

07:57 – Top-line performance: PathChat+ reaches expert-level performance in 4 of 6 tasks.

09:41 – The benchmarking trap of data leakage and why DALPHIN’s sequestered ground truth matters.

12:19 – Why real pathology diagnosis is not text-only and why macro + micro context matters.

15:26 – Tissue recognition, neoplasm detection, ambiguity, and conversational memory: how the testing was structured.

21:29 – The diagnostic personalities of the models: overcalling, undercalling, and balanced behavior.

24:36 – Rare cancers: where AI copilots still fall short of expert human performance.

28:00 – Why binary outputs are not enough when pathology often lives in uncertainty.

31:37 – Anchoring bias and conversational memory: how early hallucinations can keep propagating.

37:11 – Why these tools should be treated as co-pilots, not autopilots.

40:29 – Resources for beginners: Digital Pathology 101 and continued AI literacy.

Resources mentioned

• DALPHIN preprint: arXiv:2605.03544v1
• DALPHIN evaluation platform: dalphin.grand-challenge.org
• PathChat+ pathology-specific AI model discussed in the benchmark.
• Digital Pathology 101 free eBook by Dr. Aleksandra Zuraw.
• Educational streams on tissue recognition and computer vision literacy mentioned in the session.

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What happens when a pathology AI model misses the tissue before it even begins—and can better segmentation, education, and virtual staining close the gaps?

In DigiPath Digest #47, I review four recent studies that expose both the promise and the weak points of AI-assisted digital pathology. We start with a step that sounds simple: detecting tissue on a whole slide image. Yet when that first step fails, the downstream algorithm may miss cancer entirely.

From there, I look at a practical resource designed to improve communication between pathologists and computational scientists, a self-refining Segment Anything Model that reduces the burden of pixel-perfect nuclear annotations, and a virtual HER2 immunohistochemistry method generated from H&E images.

The common lesson? Digital pathology AI is only as reliable as the workflow underneath it. Tissue processing, scanning, segmentation, annotation, model design, and clinical interpretation all matter. Every step needs validation.

Discussion highlights

00:00 – Welcome to DigiPath Digest #47 and an overview of the four papers

02:31 – Why tissue detection is the foundation of pathology AI workflows

05:01 – A large prostate pathology study using 33,823 whole slide images for tissue detection and 70,000 images for downstream Gleason grading

06:28 – Classical thresholding missed tissue completely on 118 slides, compared with 24 slides using U-Net++ tissue detection

07:51 – How artifacts and uneven slide color can disrupt classical detection methods

09:59 – AI-based tissue detection reduced total failures by 79% but didn’t eliminate them

10:39 – Why validating only the final algorithm output can hide upstream sources of error

12:05 – Tissue detection choice produced clinically significant differences in final Gleason grade in 3.5% of malignant slides

13:33 – The communication gap between pathology and computer science—and why it still slows progress

15:29 – “Decoding Digital Histopathology” as an accessible guide for computational researchers entering pathology

18:03 – Digital Pathology 101, my free companion resource for anyone starting or continuing a digital pathology journey

20:18 – From collected tissue to glass slide, whole slide image, interpretation, and computational analysis

25:08 – The role of TCGA and the growth of digital pathology datasets across clinical, veterinary, preclinical, and research settings

27:14 – Why pixel-level nuclear annotation remains a bottleneck for deep learning

30:39 – Replacing detailed nuclear outlines with simple point prompts

31:23 – How the self-refining SAM framework uses sparse labels, contrastive learning, and a correction loop

33:25 – Do we really need to keep annotating the same structures for every new model?

37:49 – Generating virtual HER2 IHC from H&E with a score-aware, non-contrastive multitask model

39:53 – Study results: 83.01% accuracy for virtual IHC alone and 97.85% accuracy when H&E and virtual IHC were combined

40:56 – Where virtual IHC may fit as a complementary or triage tool—and why confirmatory testing still matters

44:06 – My perspective on trust, interpretation, and the difference between predicting IHC and predicting molecular alterations

46:17 – Final thoughts and an invitation to continue the discussion

Resources mentioned

• “Impact of Tissue Detection on Diagnostic Artificial Intelligence Algorithms in Prostate Digital Pathology” – Scientific Reports
• “Decoding Digital Histopathology: The Building Blocks for Computational Researchers” – PLOS Digital Health
• “Self-Refining Segment Anything Model for Nuclear Segmentation: A Contrastive Learning Approach to Label-Efficient Pathological Imaging” – Diagnostics
• “HER2 Score-Aware Virtual Immunohistochemistry via Non-Contrastive Multitask Translation” – Diagnostics
• Digital Pathology 101: All You Need to Know to Start and Continue Your Digital Pathology Journey
• European Society of Digital and Integrative Pathology
• The Cancer Genome Atlas (TCGA)

If you work in pathology, computational science, image analysis, or AI development, this episode is a useful reminder to look beyond the headline performance metric. The errors that matter may begin much earlier in the workflow.

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Do you really need a scanner, whole slide images, and AI infrastructure before you can start in digital pathology?

In this episode, I argue that you do not.

I’m Dr. Aleksandra Zuraw, veterinary pathologist and digital pathology educator, and this talk is about a belief I hear all the time: I don’t have the tools yet, so there is no point learning digital pathology. I used to think that too. When I was training in Berlin, there was one Leica 6-slide scanner, and it felt like digital pathology was only for a small group of chosen people. That experience made the field feel distant, exclusive, and not really available to beginners.

What changed for me was not a new scanner. It was a small project.

I needed a more consistent way to quantify a senescence marker in archived skin samples, so I used a microscope camera, captured images, opened them in Microsoft Paint, and manually marked cells with colored dots. It was scrappy. Very low tech. But it was also digital, consistent, and verifiable. That project became my first real step into digital pathology and helped me get my first job in the field, where I worked between pathologists and image analysis scientists on biomarker quantification and patient stratification problems.

That is the core point of this episode: knowledge unlocks technology.

Scanners matter. AI tools matter. But the deeper bottleneck is whether enough people understand how to use these tools, ask good questions, and connect pathology expertise with digital workflows. That is why this episode is really about readiness. Not readiness of the hardware. Readiness of the people.

I also talk about Dr. Taladzer from Pakistan, whose story makes this point even more clearly. At the time, Pakistan had around 220 million people, about 500 pathologists, and zero scanners. She still started learning digital pathology during COVID using a microscope and camera, joined the Digital Pathology Association, taught herself from papers and online resources, and kept going even after multiple AI vendors rejected her because she did not have whole slide images. Eventually, she found a DIY image analysis platform, learned to annotate and train models on static images, completed projects quickly, and went on to publish more than 10 digital pathology papers without ever using WSI.

Why should you listen?

Because this episode is for pathologists and lab leaders who are interested in digital pathology but still feel stuck at the beginning. It is for people waiting for permission, perfect infrastructure, or a formal roadmap. And it is for trailblazers who came back from a meeting or conference energized, but need a practical way to turn that energy into action before it fades.

I also address an important AI question near the end: How do we know an AI model is good enough for pathology? I talk about why models are only as good as the pathologist annotations used to train them, why concordance between pathologists matters, how orthogonal labels like IHC can improve model quality, and why pathologists still need to stay in the loop as these systems develop and get deployed.

If you are trying to figure out where to start, this episode gives you a practical answer: start where you are. Start with what you have. Start learning now.

Episode Highlights

00:00 – Why the real barrier to digital pathology is usually not the hardware
00:33 – What it feels like to be at the beginning of the digital pathology journey
02:50 – My first practical digital pathology project using a microscope camera and Microsoft Paint
05:37 – How that low-tech project led to my first digital pathology job
08:52 – Why knowledge, not infrastructure, is the real unlock
09:57 – Dr. Taladzer’s story: starting digital pathology in Pakistan with zero scanners
12:03 – What happened after repeated vendor rejection and why persistence mattered
14:39 – The “forgetting loop” vs the “commitment loop” after conferences
16:48 – Practical next steps: book, PubMed alerts, journal clubs, webinars, vendor resources
18:52 – Why I believe digital pathology is the gateway to faster diagnosis
20:00 – How to think about whether an AI model is really ready for pathology

Resources Mentioned

• Digital Pathology 101 – free book recommended as a starting point for learning digital pathology.
• Digital Pathology Association – mentioned as a learning resource and professional community.
• PubMed alerts for AI and digital pathology.
• Journal clubs – mentioned as one way to keep learning consistently.
• Webinars and vendor resources – suggested as practical ways to keep building knowledge.
• A4A – the DIY image analysis platform that supported Dr. Taladzer’s early work with static image annotation and model training.

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Why are pathology vendors still speaking different image languages when radiology solved that problem decades ago?

In this episode of DigiPath Digest #46, I talk through four papers that all point to a bigger issue in digital pathology: we are not only dealing with better algorithms. We are dealing with interoperability, workflow design, explainability, and whether the field is actually ready to use these tools well.

I start with DICOM in digital pathology, because I think this is still one of the most important infrastructure questions in the field. Digital pathology has clear value for consultation, image analysis, archival, and workflow, but vendor-specific whole slide image formats still create silos. In the episode, I explain why DICOM matters, why adoption is still low, how the multi-resolution pyramid works, and why this is really about enterprise imaging and future-proofing, not just file conversion.

Then I move into kidney transplant rejection, where the paper makes a strong case for multimodal precision diagnostics. Creatinine is late. Antibody testing can miss important biology. Biopsies can miss the area that matters. So the opportunity is not to replace pathology, but to combine biomarkers, biopsy, and machine learning in a way that is more useful than any one signal alone. I also talk about explainability here, because if a model gives a risk score, we need to know what contributed to it.

The third paper focuses on perineural invasion in solid tumors, and I liked this one a lot because it shows how AI can help standardize something that is clinically important but still inconsistently detected and reported. Perineural invasion is not just a passive pathway of spread. The biology is more active than that, and the quantification can go far beyond a simple yes-or-no answer. This is a good example of where digital pathology can do something humans cannot realistically do by eye at scale.

The last paper is on gastric cancer immunohistochemistry biomarkers and advanced quantification, including HER2, PD-L1, mismatch repair, and CLDN18.2. This section is really about complexity. We are now asking pathologists to visually score biology that is getting harder and harder to summarize consistently, especially when markers, spatial context, and multiplexing all start to matter at once. I make the case that computational pathology is becoming necessary here, not because pathologists are failing, but because the biology is outgrowing purely visual workflows.

What ties these four papers together is simple: digital pathology is not only about remote reading anymore. It is about interoperability, quantification, explainable AI, and making pathology more precise in places where the old workflow is reaching its limit. If you are a pathologist, lab leader, or digital pathology trailblazer trying to figure out what actually matters right now, this episode will help you connect the dots.

Episode Highlights

07:41 – Why DICOM still matters if we want digital pathology systems to work together.
14:39 – Current adoption of SVS, MRXS, and DICOM, and why DICOM is still lagging.
16:44 – How the DICOM whole slide image pyramid works and why it matters for workflow.
24:29 – Why kidney transplant rejection is still difficult to diagnose with any single marker.
29:18 – Why perineural invasion is clinically important and still inconsistently reported.
34:44 – How AI can quantify tumor-nerve relationships more consistently than visual review alone.
46:39 – Why gastric cancer biomarker scoring is getting too complex for purely visual workflows.
54:55 – Multiplexing, spatial biology, and why explainable AI matters in biomarker interpretation.
01:04:01 – What is really blocking digital pathology adoption: cost, workflow, regulation, or mindset?

Resources mentioned

• DICOM / digital pathology interoperability paper
https://pubmed.ncbi.nlm.nih.gov/42093730/
• Kidney transplant rejection, biomarkers, and artificial intelligence
https://pubmed.ncbi.nlm.nih.gov/42073482/
• Perineural invasion in solid tumors with AI and machine learning applications
https://pubmed.ncbi.nlm.nih.gov/42100436/
• Gastric cancer IHC biomarkers, advanced detection methods, and perspectives
https://pubmed.ncbi.nlm.nih.gov/42075555/
• Digital Pathology Place
https://digitalpathologyplace.com
• Digital Pathology 101
Free PDF book mentioned at the end of the episode through Digital Pathology Place.

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What if the most frightening part of a pathology report is not the word cancer, but the silence that follows?

In this episode of the Digital Pathology Podcast, Dr. Aleksandra Zuraw talks with Michele Mitchell—breast cancer survivor, caregiver, national patient advocate, and longtime volunteer across Michigan Medicine, ASCP, the Digital Pathology Association, and MyPathologyReport.ca—about what happened when she saw her own cancer slide years after treatment. That moment changed how she understood her disease, her risk, and her role as a patient advocate.

This is not just a patient story. It is a digital pathology implementation story.

The episode looks at how digital pathology removes practical barriers to sharing slides, why pathology clinics matter, and what becomes possible when pathologists move from being hidden in the background to becoming direct contributors to patient understanding. Michelle and Dr. Aleks talk through the communication gap around pathology reports, the emotional cost of delayed explanation, and the real-world workflow of pathology clinic visits built to help patients review their slides with the pathologist who made the diagnosis.

They also discuss what the 21st Century Cures Act changed for patients, why immediate access to reports without interpretation can still create fear, and how pathology clinics can bridge the gap between raw data and real understanding. The conversation gets practical too: how patients can request a pathology clinic visit, what virtual pathology consults can look like, how billing and workflow concerns are already being addressed, and why the infrastructure question is smaller than many people assume.

If you work in digital pathology, pathology informatics, patient communication, or implementation, this episode is a reminder that visibility is not extra. It is part of the value proposition. And for pathologists who worry this is too far outside the traditional role, the episode offers a grounded counterpoint: the workflows, templates, billing structures, and virtual options already exist.

Highlights

• 00:00 – Why pathology needs to become more patient-centered
Michele frames the core problem clearly: what often scares patients is not only cancer, but the silence around the diagnosis.
• 00:34 – How digital pathology changes the patient experience
Digital slides make it possible for patients to see their diagnosis, compare normal and abnormal tissue, and ask better questions.
• 11:13 – What happened when Michele saw her cancer for the first time
More than a decade after treatment, seeing her own slide changed how she understood her grade, her risk, and her daily health decisions.
• 16:19 – Why visual pathology can change adherence and lifestyle
Michele explains how the image-based explanation became a practical turning point, not just an emotional one.
• 20:43 – The case for direct pathologist-patient communication
The episode reviews why this can improve clarity, treatment understanding, clinic efficiency, and even professional satisfaction for pathologists.
• 38:40 – What a pathology clinic actually looks like
From preparation and consent to slide review, plain language, empathy, and follow-up, the workflow is much more concrete than many people assume.
• 45:35 – ASCP’s certification workshop for pathology clinics
Michele describes the national effort to make pathology clinics reproducible, scalable, and easier to implement.
• 49:32 – What the 21st Century Cures Act changed
Patients now get near real-time access to reports, but that access still needs interpretation, context, and support.
• 01:03:23 – Pushback, logistics, and why the barriers are not where people think
Time, reimbursement, scheduling, and virtual setup are addressed directly with examples already in practice.
• 01:16:57 – The future: patient-friendly reports, AI, and pathology as part of the care team
The episode closes on a practical vision: not hype, but tools and workflows that already exist and can be connected now.

Resources mentioned

• Digital Pathology Place – website and educational platform referenced by Dr. Aleks as the home for her work and resources.
• Digital Pathology 101 – Dr. Aleks’s book, referenced in the broader discussion of patient and pathologist education.
• Michigan Medicine breast pathology clinic – launched in 2023 as a patient-facing breast pathology clinic model.
• ASCP pathology clinic certification workshop – national workshop co-developed to help institutions build pathology clinics.
• 21st Century Cures Act – legal framework behind near real-time patient access to pathology reports and related health data.
• MyPathologyReport.ca – patient-friendly pathology education resource reviewed with patient advocate involvement.
• American Cancer Society Reach to Recovery – support resource mentioned for breast cancer patients.
• Scanslated – patient-friendly report interface discussed as part of a future-facing model for pathology communication.
• Virtual pathology consults/telehealth setup – discussed as a scalable way to lower implementation friction.

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DigiPath Digest #45 asks a practical question: can AI in pathology move from correlation to real clinical use? In this episode, I review four papers that push on that question from different angles: computational pathology moving toward morphology-driven molecular inference, the current state of digital cytopathology and AI, multi-omics and precision oncology in hepatocellular carcinoma, and AI literacy in veterinary education. What ties them together is not model performance alone. It is the harder question of validation, workflow fit, quantitative use, ethics, and human oversight.

In the first paper, I talk about computational pathology as more than pattern recognition. The focus is on morphology-driven molecular inference, digital biomarkers, and why spatial omics matters as biological ground truth. I also discuss why continuous quantitative scoring is more useful than forcing biology into rough scoring buckets.

The second paper focuses on digital cytopathology. Cytology was early for FDA-cleared AI in cervical screening, but non-gynecologic cytology is still much harder to digitize because of specimen variability and workflow complexity. I also cover telecytology, rapid onsite evaluation, automation, and quality control.

The third paper looks at hepatocellular carcinoma and AI-driven precision oncology. This part is about using AI and machine learning to integrate genomics, transcriptomics, proteomics, metabolomics, radiomics, and pathology to support biomarker discovery, tumor microenvironment analysis, and treatment stratification.

The fourth paper may be the most broadly useful. It proposes an AI literacy curriculum for veterinary education that covers AI fundamentals, machine learning evaluation, LLMs, ethics, liability, and academic integrity. I think that matters far beyond veterinary medicine, because if clinicians are expected to use AI tools responsibly, AI literacy cannot stay optional.

Highlights
00:01 Welcome and overview of the four papers
03:02 Computational pathology and morphology-driven molecular inference
11:01 Digital cytopathology, telecytology, and QC
20:47 AI/ML in hepatocellular carcinoma precision oncology
31:04 AI literacy in veterinary education
47:42 Final takeaways and Digital Pathology 101 update

Resources

• Computational Pathology as a Mechanistic Discipline: From Morphology to Molecular Data
https://pubmed.ncbi.nlm.nih.gov/42052846/

• Advances in Digital Cytopathology and Artificial Intelligence Applications
https://pubmed.ncbi.nlm.nih.gov/42046894/

• Navigating the Labyrinth of Hepatocellular Carcinoma: Leveraging AI/ML for Precision Oncology
https://pubmed.ncbi.nlm.nih.gov/42065059/

• Curriculum Framework for Artificial Intelligence Literacy in Veterinary Education
Front Vet Sci. 2026;13:1801756

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