Digital Pathology Podcast

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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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