Digital Pathology Podcast

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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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